U.S. patent application number 16/971715 was filed with the patent office on 2020-12-17 for sorting support apparatus, sorting support system, sorting support method, and program.
This patent application is currently assigned to NEC Corporation. The applicant listed for this patent is NEC Corporation. Invention is credited to Kazuyuki IKEMURA, Masahiro KAJI, Kaori TAKEUCHI, Shinichi YOSHITSUNE.
Application Number | 20200393390 16/971715 |
Document ID | / |
Family ID | 1000005073267 |
Filed Date | 2020-12-17 |
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United States Patent
Application |
20200393390 |
Kind Code |
A1 |
KAJI; Masahiro ; et
al. |
December 17, 2020 |
SORTING SUPPORT APPARATUS, SORTING SUPPORT SYSTEM, SORTING SUPPORT
METHOD, AND PROGRAM
Abstract
A sorting support apparatus is provided with: an input part that
inputs a transmission image obtained by radiating an inspection
target with electromagnetic waves; a storage part that stores a
plurality of learning models optimized respectively for at least
one article and being associated with an assumed usage condition;
and a determination part that selects one of the learning models
based on a specified usage condition and uses the learning model to
determine whether or not the one or more articles is contained in
the inspection target.
Inventors: |
KAJI; Masahiro; (Tokyo,
JP) ; IKEMURA; Kazuyuki; (Tokyo, JP) ;
YOSHITSUNE; Shinichi; (Tokyo, JP) ; TAKEUCHI;
Kaori; (Tokyo, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEC Corporation |
Tokyo |
|
JP |
|
|
Assignee: |
NEC Corporation
Tokyo
JP
|
Family ID: |
1000005073267 |
Appl. No.: |
16/971715 |
Filed: |
March 28, 2019 |
PCT Filed: |
March 28, 2019 |
PCT NO: |
PCT/JP2019/013547 |
371 Date: |
August 21, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2223/401 20130101;
G06N 20/00 20190101; G01N 2223/04 20130101; G06K 9/00771 20130101;
G01N 23/10 20130101; G01N 23/04 20130101; G01N 2223/639 20130101;
G06N 5/04 20130101; G01N 23/083 20130101 |
International
Class: |
G01N 23/10 20060101
G01N023/10; G01N 23/04 20060101 G01N023/04; G01N 23/083 20060101
G01N023/083; G06N 20/00 20060101 G06N020/00; G06N 5/04 20060101
G06N005/04; G06K 9/00 20060101 G06K009/00 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 29, 2018 |
JP |
2018-066026 |
Claims
1. A sorting support apparatus comprising: an input part configured
to input a transmission image obtained by radiating an inspection
target with electromagnetic waves; a storage part configured to
store a plurality of learning models optimized respectively for at
least one article and being associated with an assumed usage
condition; and a determination part configured to select one of the
learning models based on a specified usage condition and uses the
learning model to determine whether or not the one or more articles
is contained in the inspection target.
2. The sorting support apparatus according to claim 1, wherein the
learning model is created in accordance with a trend of handled
goods at a location where the sorting support apparatus is
disposed.
3. The sorting support apparatus according to claim 1, wherein the
learning model is created in accordance with a trend of handled
goods in a time-period in which sorting is performed.
4. The sorting support apparatus according to claim 1, wherein the
learning model is created in accordance with a trend of handled
goods according to sender location.
5. The sorting support apparatus according to claim 1, wherein it
is possible to change a threshold for determining, in the
determination part, whether or not the at least one article is
included.
6. A sorting support system wherein the sorting support apparatus
of claim 1 is disposed at multiple stages to determine in a
stepwise manner whether or not the at least one article is
included, using different learning models.
7. The sorting support system according to claim 6, configured so
that a sorting support apparatus at a first stage uses a learning
model optimized for sorting paper and non-paper articles, and
sorting support apparatuses at second and following stages use
learning models optimized for further sorting the non-paper
articles.
8. A sorting support method, wherein a sorting support apparatus
that comprises an input part configured to input a transmission
image obtained by radiating an inspection target with
electromagnetic waves, and a storage part configured to store a
plurality of learning models optimized respectively for at least
one article and being associated with an assumed usage condition:
selects one of the learning models based on a specified usage
condition; and determines, by using the learning model, whether or
not the one or more articles is included in the inspection
target.
9. (canceled)
10. The sorting support apparatus according to claim 2, wherein the
learning model is created in accordance with a trend of handled
goods in a time-period in which sorting is performed.
11. The sorting support apparatus according to claim 2, wherein the
learning model is created in accordance with a trend of handled
goods according to sender location.
12. The sorting support apparatus according to claim 3, wherein the
learning model is created in accordance with a trend of handled
goods according to sender location.
13. The sorting support apparatus according to claim 2, wherein it
is possible to change a threshold for determining, in the
determination part, whether or not the at least one article is
included.
14. The sorting support apparatus according to claim 3, wherein it
is possible to change a threshold for determining, in the
determination part, whether or not the at least one article is
included.
15. The sorting support apparatus according to claim 4, wherein it
is possible to change a threshold for determining, in the
determination part, whether or not the at least one article is
included.
16. A sorting support system wherein the sorting support apparatus
of claim 2 is disposed at multiple stages to determine in a
stepwise manner whether or not the at least one article is
included, using different learning models.
17. A sorting support system wherein the sorting support apparatus
of claim 3 is disposed at multiple stages to determine in a
stepwise manner whether or not the at least one article is
included, using different learning models.
18. A sorting support system wherein the sorting support apparatus
of claim 4 is disposed at multiple stages to determine in a
stepwise manner whether or not the at least one article is
included, using different learning models.
19. A sorting support system wherein the sorting support apparatus
of claim 5 is disposed at multiple stages to determine in a
stepwise manner whether or not the at least one article is
included, using different learning models.
20. The sorting support system according to claim 16, configured so
that a sorting support apparatus at a first stage uses a learning
model optimized for sorting paper and non-paper articles, and
sorting support apparatuses at second and following stages use
learning models optimized for further sorting the non-paper
articles.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a National Stage of International
Application No. PCT/JP2019/013547 filed Mar. 28, 2019, claiming
priority based on Japanese Patent Application No. 2018-066026
(filed on Mar. 29, 2018) the content of which is hereby
incorporated in its entirety by reference into this specification.
The present disclosure relates to a sorting support apparatus, a
sorting support system, a sorting support method, and a
program.
BACKGROUND
[0002] Among articles imported into Japan from abroad are various
types of prohibited articles and restricted articles. Prohibited
articles include, for example, firearms, explosives, narcotics and
specified drugs. Inspection of these is carried out at customs at
various locations and post offices that have a customs facility for
foreign mail (referred to below as "foreign mail customs post
office").
[0003] Similar restrictions also apply to general goods within
Japan. Additionally, there are articles such as lithium batteries,
explosives and the like which are prohibited from being transported
by air, as hazardous materials for air transportation. These
inspections are carried out at customs offices or distributor
locations.
[0004] X-ray scanning apparatuses are used in these inspections.
For example, Patent Literature (PTL) 1 discloses a system, a
method, a device and an apparatus for determining whether an
individual (22) is carrying a suspicious concealed object (25) in
their clothing. The same literature discloses detecting a
suspicious concealed object (25) through inspection by
electromagnetic radiation in a range of 200 MHz-1 THz, by a process
of receiving image data corresponding to the intensity of reflected
radiation and depth difference of a reflecting surface.
[PTL 1]
Japanese Patent Kohyo Publication No. 2007-517275A
SUMMARY
[0005] At customs, besides X-ray scanning, inspections are carried
out at the same time with drug-sniffing dogs, and from the
viewpoint of whether the weight and size of declared contents are
in conformity. However, in recent years prohibited articles and
restricted articles are cleverly carried, and in such cases, it is
necessary to rely on inspections by staff, so that staff work load
is increasing.
[0006] It is an object of the present disclosure to provide a
sorting support apparatus, a sorting support system, a sorting
support method, and a program, that can contribute to reducing the
load of inspection work in the abovementioned logistics
process.
[0007] According to a first aspect, the disclosure provides a
sorting support apparatus comprising: an input part that inputs a
transmission image obtained by radiating an inspection target with
electromagnetic waves; a storage part that stores a plurality of
learning models optimized respectively for at least one article and
being associated with an assumed usage condition; and a
determination part that selects one of the learning models based on
a specified usage condition and uses the learning model to
determine whether or not the one or more articles is contained in
the inspection target.
[0008] According to a second aspect, the disclosure provides a
sorting support system wherein the sorting support apparatus is
disposed at multiple stages to determine in a stepwise manner
whether or not the at least one article is included, using
different learning models.
[0009] According to a third aspect, the disclosure provides a
sorting support method, wherein a sorting support apparatus
comprising an input part that inputs a transmission image obtained
by radiating an inspection target with electromagnetic waves, and a
storage part that stores a plurality of learning models optimized
respectively for at least one article and being associated with an
assumed usage condition, selects one of the learning models based
on a specified usage condition, and determines, by using the
learning model, whether or not the one or more articles is included
in the inspection target. The method is associated with a
particular machine that is a sorting support apparatus which is
provided with an input part, a storage part and a processor that
implements various processing steps.
[0010] According to a fourth aspect, the disclosure provides a
program that causes a computer, installed in a sorting support
apparatus comprising an input part that inputs a transmission image
obtained by radiating an inspection target with electromagnetic
waves, and a storage part that stores a plurality of learning
models optimized respectively for at least one article and being
associated with an assumed usage condition, to execute processing
comprising selecting one of the learning models based on a
specified usage condition, and determining, by using the learning
model, whether or not the one or more articles is included in the
inspection target. It is to be noted that this program may be
recorded on a computer-readable (non-transitory) storage medium.
That is, the present disclosure may be embodied as a computer
program product. The program may be inputted via an input apparatus
or communication interface from outside to a computer apparatus,
stored in the storage apparatus, and activated according to
prescribed steps or processes of a processor. The program may
display a processing result thereof including an intermediate state
as necessary via a display apparatus for each stage, or may
communicate with the outside via a communication interface. A
computer apparatus for this is typically provided with, as an
example, a processor that can be interconnected therewith by a bus,
a storage apparatus, an input apparatus, a communication interface,
and a display apparatus as necessary.
DISCLOSURE
[0011] The meritorious effects of the present disclosure are
summarized as follows.
[0012] The disclosure can contribute to reducing the load required
in inspection work in the abovementioned logistics process. That
is, the present disclosure transforms a sorting support apparatus
described in the background technology into one that may
dramatically reduce staff work load.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a diagram illustrating a configuration of an
example embodiment of the present disclosure.
[0014] FIG. 2 is a diagram showing an overall configuration of a
system including a sorting support apparatus in a first example
embodiment of the disclosure.
[0015] FIG. 3 is a functional block diagram showing a configuration
of the sorting support apparatus in the first example embodiment of
the disclosure.
[0016] FIG. 4 is a diagram showing an example of a set of learning
models held by the sorting support apparatus in the first example
embodiment of the disclosure.
[0017] FIG. 5 is a flowchart representing operations of the sorting
support apparatus in the first example embodiment of the
disclosure.
[0018] FIG. 6 is a diagram showing an example of a set of learning
models held by the sorting support apparatus in a second example
embodiment of the disclosure.
[0019] FIG. 7 is a functional block diagram showing a configuration
of the sorting support apparatus in a third example embodiment of
the disclosure.
[0020] FIG. 8 is a diagram showing an example of a set of learning
models held by the sorting support apparatus in the third example
embodiment of the disclosure.
[0021] FIG. 9 is a flowchart representing operations of the sorting
support apparatus in the third example embodiment of the
disclosure.
[0022] FIG. 10 is a diagram showing an overall configuration of a
system including the sorting support apparatus in a fourth example
embodiment of the disclosure.
[0023] FIG. 11 is a diagram showing a configuration of a computer
configuring the sorting support apparatus of the disclosure.
PREFERRED MODES
[0024] First, a description is given of an outline of an example
embodiment of the present disclosure, making reference to the
drawings. It is to be noted that reference symbols in the drawings
attached to this outline are added to respective elements for
convenience as examples in order to aid understanding, and are not
intended to limit the present disclosure to modes illustrated in
the drawings. Connection lines between blocks in the diagrams
referred to in the following description include both
unidirectional and bidirectional. Unidirectional arrows
schematically show flow of main signals (data), but do not exclude
bidirectionality. There are ports and interfaces at input output
connection points of respective blocks in the diagrams, but
illustrations thereof are omitted. A program is executed via a
computer apparatus, and the computer apparatus is provided with,
for example, a processor, a storage apparatus, an input apparatus,
a communication interface, and a display apparatus as necessary.
The computer apparatus is configured to enable communication,
either wireless or wired, with equipment (including a computer)
within or outside the apparatus via a communication interface.
[0025] The present disclosure, in an example embodiment thereof as
shown in FIG. 1, may be implemented as a sorting support apparatus
10 provided with an input part 11, a storage part 12 and a
determination part 13. More specifically, the input part 11 inputs
a transmission image from an apparatus that obtains the
transmission image by radiating an inspection target with
electromagnetic waves.
[0026] The storage part 12 stores a plurality of learning models A
to C, associated with assumed usage conditions and optimized
respectively for at least 1 article. The learning models A to C may
be created using AI (Artificial Intelligence) as represented by
deep learning, using transmission images of the respective articles
as instructor data.
[0027] The determination part 13 selects one of the learning
models, based on a specified usage condition, uses the learning
model to determine whether or not the at least one article is
included in the inspection target, and outputs a result
thereof.
[0028] The sorting support apparatus of the present disclosure is
installed at various sites where inspection of contents of goods is
needed. Articles for which detection is anticipated by the present
apparatus are different at each of these sites. Therefore, by
creating the abovementioned learning models A to C, using these
articles for which detection is anticipated as learning data with
instructor, it is possible to perform sorting support specialized
for each site.
[0029] For example, for a certain logistics operator, in a case
where there is concern about a small sized electrical appliance
received with a built-in lithium battery, a learning model is
prepared that is optimized to enable a lithium battery or small
sized electrical appliance to be suitably detected. At an air
transport goods inspection site of the logistics operator, the
sorting support apparatus makes a determination regarding the
inspection target by using this learning model corresponding to the
usage condition (lithium battery detection).
[0030] For example, in the same way for a certain logistics
operator, in a case where there is concern about goods being
receiving that contain a living creature such as an insect or a
small animal, a learning model is prepared that is optimized to
enable these living creatures to be suitably detected. At a goods
inspection site of the logistics operator, the sorting support
apparatus makes a determination regarding the inspection target by
using this learning model corresponding to the usage condition
(living creature detection).
[0031] It is to be noted that in selection of the learning model, a
user of the sorting support apparatus 10 may explicitly specify a
usage condition, or the sorting support apparatus 10 may determine
a usage condition based on information inputted by the user of the
sorting support apparatus 10, to select the learning model.
[0032] As described above, according to the present disclosure, it
is possible to improve the accuracy of sorting by the sorting
support apparatus 10, and to reduce misjudgments. A reason for this
is due to the employment of a configuration in which selection is
performed of a learning model used in the sorting support apparatus
10 based on a specified usage condition.
First Exemplary Embodiment
[0033] Continuing, a detailed description referring to the drawings
is given concerning a first example embodiment of the present
disclosure assuming supporting an inspection operation on a post
office article at a foreign mail customs post office, using an
X-ray as an electromagnetic wave for obtaining a transmission
image. FIG. 2 is a diagram showing an overall configuration of a
system including a sorting support apparatus of the first example
embodiment of the disclosure. FIG. 2 shows the sorting support
apparatus 100 in a form straddling a belt conveyor 190 that moves a
package 300. In the example of FIG. 2 the sorting support apparatus
100 is connected to a rotating light 170 and to an operation
terminal 180.
[0034] In a case of determining that a specific article is included
in the package 300 that is an inspection target, the sorting
support apparatus 100 activates the flashing light 170 and also
performs an operation to stop the belt conveyor 190. Clearly, it is
also possible to employ a mode where, instead of the flashing light
170, a speaker is connected and a buzzer or a voice announcement is
outputted. Instead of these it is possible to employ a mode in
which a warning is displayed on a console or dashboard (information
display program) or the like on a display screen of an operation
terminal. An inspection staff member accepts these outputs,
confirms whether or not the weight of the package 300 matches the
contents, and performs a detailed inspection by opening it. In
order to reduce the workload of the inspection staff member in
these processes, an apparatus which automatically sorts a package
determined to include a specific article may be disposed
therebeside.
[0035] The operation terminal 180 is an apparatus such as a
personal computer (PC) used for setting a learning model, described
later, in the sorting support apparatus 100, or for setting a usage
condition of the sorting support apparatus 100. Using the operation
terminal 180, the sorting support apparatus 100 may be enabled to
set an operation in a case of a determination that a specific
article is included in the package.
[0036] FIG. 3 is a functional block diagram showing a configuration
of the sorting support apparatus in the first example embodiment of
the disclosure. FIG. 3 shows the sorting support apparatus 100
provided with an input part 101, a storage part 102 and a
determination part 103.
[0037] When an X-ray image is inputted from an X-ray camera 104
disposed inside an internal casing of a sorting support apparatus,
the input part 101 outputs to the determination part 103. At this
time, the input part 101 may perform essential pre-processing in
order to improve determination accuracy of the determination part
103.
[0038] The storage part 102 stores a plurality of learning models
that are associated with assumed usage conditions and optimized
respectively for at least 1 article. The learning models may be
created using AI as represented by deep learning, using a large
amount of actually obtained X-ray images as instructor data.
[0039] FIG. 4 is an example of a set of learning models according
to location (sorting site) stored in the storage part 102. A
learning model A in FIG. 4 is a learning model created assuming
usage at a foreign mail customs post office (this type of foreign
mail customs post office is described below as "foreign mail
customs post office A") handling a large amount of surface mail.
Specifically, the learning model A is created using image data of
various types of prohibited articles and restricted articles
actually found at a foreign mail customs post office A, to
calculate a characteristic amount thereof. Similarly, a learning
model B is a learning model created assuming usage at a foreign
mail customs post office (described below as "foreign mail customs
post office B") handling a large amount of air mail or EMS (Express
Mail Service). Specifically, the learning model B is created using
image data of various types of prohibited articles and restricted
articles actually found at a foreign mail customs post office B, to
calculate a characteristic amount thereof. There is a great variety
of packages (post office articles) that arrive at foreign mail
customs post offices, but by systematic elimination of such
learning models, it is possible to improve detection accuracy of
specific articles and to reduce misjudgment rates, without
improving the required performance of the sorting support
apparatus. It is to be noted that clearly a learning model outside
of the learning models A and B may be provided, according to
differences of articles handled at each foreign mail customs post
office.
[0040] It is to be noted that the learning model SP of FIG. 4 is a
learning model that assumes prioritized inspection articles. For
example, at each customs point, a time-period is established and
strengthening of inspection of specific articles is carried out.
The learning model SP is a learning model created in order to
strengthen the inspection of articles set as prioritized inspection
articles. By using this type of learning model SP alone, or using
learning models A and B together, it is possible to handle
strengthening of inspection of prioritized inspection articles. It
is to be noted that the set of learning models shown in FIG. 4 is
merely an example thereof, and the set of learning models may be
changed according to location (sorting location).
[0041] The determination part 103 selects a learning model based on
a usage condition of the sorting support apparatus 100 inputted by
the operation terminal 180 and performs inspection of a package.
For example, the determination part 103 uses the learning model to
inspect an X-ray image and calculate the probability (likelihood)
that a specified article is included in the inspection target. In a
case where the probability (likelihood) is greater than or equal to
a prescribed threshold, the determination part 103 determines that
the article is included in the inspection target, and activates the
rotating light 170 along with performing an operation to stop the
belt conveyor 190.
[0042] Continuing, a detailed description referring to the drawings
is given concerning operations of the present example embodiment.
FIG. 5 is a flow chart representing operations of the sorting
support apparatus in the first example embodiment of the
disclosure. Referring to FIG. 5, first, a learning model is
selected at the operation terminal 180 and an inspection is started
(step S001). In this way, the belt conveyor 190 is activated and an
image taken by the X-ray camera 104 is sent to the sorting support
apparatus 100.
[0043] When the X-ray image is inputted (step S002), the sorting
support apparatus 100 uses the selected learning model to confirm
whether or not a specified article is included in the X-ray image
(step S003). Here, in a case where it is determined that a
specified article is included in the X-ray image (YES in step
S004), the sorting support apparatus 100 activates the rotating
light 170, along with performing an operation to stop the belt
conveyor 190 (step S005).
[0044] An inspection staff member who receives the notification
performs a detailed check of a package 300 for which a
determination has been made that a specified article is included in
the X-ray image, and performs an inspection to open the package as
necessary.
[0045] Meanwhile, in a case where it is determined that a specified
article is not included in the X-ray image (step S004), the sorting
support apparatus 100 continues with input of an X-ray image and
making a determination until the inspection is finished
(continuation steps S002 to S006).
[0046] It is to be noted that to finish the inspection, a
determination may be made, for example, by input of an
end-operation by the operation terminal 180 or a prescribed time
being reached.
[0047] As described above, according to the present example
embodiment it is possible to dramatically improve the determination
accuracy of the sorting support apparatus. A reason for this is
that a configuration is employed in which a plurality of learning
models are provided and a selection to be used is made in
accordance with a usage condition. Another aspect of the
improvement in determination accuracy according to the present
example embodiment is that misjudgments can be reduced.
Second Exemplary Embodiment
[0048] Continuing, a detailed description referring to the drawings
is given concerning a second example embodiment in which a
modification is added to the set of learning models stored in the
storage part 102. Since the second example embodiment can be
implemented by a configuration similar to the first example
embodiment, a description is given below centered on points of
difference.
[0049] FIG. 6 is an example of a set of learning models stored in
the storage part 102 of the sorting support apparatus 100 of the
second example embodiment. A point of difference from the set of
learning models shown in FIG. 4 is that, for the same foreign mail
customs post office, a plurality of learning models are provided,
giving consideration to the season and time-period.
[0050] A learning model A1 in FIG. 6 is a learning model created
assuming usage in the Christmas season at a foreign mail customs
post office A that handles a large amount of surface mail.
Specifically, the learning model A1 is created using image data of
prohibited articles and restricted articles prepared giving
consideration to trends among articles actually handled in the
Christmas season, at the foreign mail customs post office A.
Similarly, a learning model A2 is created using image data of
prohibited articles and restricted articles prepared giving
consideration to trends among goods actually handled outside the
Christmas season, at the foreign mail customs post office A. In
this way, by using learning models according to the Christmas
season and to other seasons, it is possible to perform sorting
giving consideration to a rapid increase in greeting cards or
presents in the Christmas season. In other seasons, by using a
learning model that does not give consideration to the presence of
greeting cards and presents, it is possible to improve sorting
accuracy.
[0051] A learning model B1 in FIG. 6 is a learning model created
assuming usage in January to March at a foreign mail customs post
office B. Similarly, a learning model B2 is a learning model
created assuming usage in April to December at a foreign mail
customs post office B. For example, in the Asian region, since
there is a change in articles handled at the Chinese new year,
using this type of learning model is effective.
[0052] As described above, according to the present example
embodiment it is possible to perform sorting support giving
consideration to change in packages according to season and
time-period. It is to be noted that in the abovementioned
description, learning models are used according to season and
time-period at foreign mail customs post office units, as in
foreign mail customs post offices A and B, but it is also possible
to employ a configuration that does not consider differences of
foreign mail customs post offices, but provides learning models
according to the season and according to the time-period, to
jointly use learning models at a plurality of foreign mail customs
post offices.
Third Exemplary Embodiment
[0053] Continuing, a detailed description referring to the drawings
is given concerning a third example embodiment in which learning
models are used giving consideration to sender location (remitter
location). Since the third example embodiment can basically be
implemented by a configuration similar to the first example
embodiment, a description is given below centering on points of
difference.
[0054] FIG. 7 is a functional block diagram showing a configuration
of a sorting support apparatus 100a in the third example embodiment
of the disclosure. A point of difference from the sorting support
apparatus 100 of the first example embodiment shown in FIG. 3 is
that a sender location recognition part 105 is added, and a
determination part 103 selects a learning model using a sender
location recognized by the sender location recognition part
105.
[0055] The sender location recognition part 105 recognizes the
sender location from a tag, an addressee label, an airline sticker,
a barcode or the like, attached to a package 300. It is to be noted
that character recognition technology may be used as a method of
recognizing the sender location. Besides the method of recognizing
sender location directly from the tag or the like, it is also
possible to adopt a configuration in which the package 300 is
identified from tracking information or query number of the package
300, and to send a query about the sender location to an external
server or the like.
[0056] FIG. 8 is an example of a set of learning models stored in
the storage part 102 of the sorting support apparatus 100a of the
third example embodiment. A point of difference from the set of
learning models shown in FIG. 4 is that, for the same foreign mail
customs post office, a plurality of learning models are provided
according to sender location.
[0057] A learning model AA in FIG. 8 is a learning model created
assuming as a target a package with country A as a sender location,
at foreign mail customs post office A that handles a large amount
of surface mail. Specifically, the learning model AA is created
using image data of prohibited articles and restricted articles
actually found in a package sent from country A at a foreign mail
customs post office. Similarly, a learning model AB is created
using image data of prohibited articles and restricted articles
actually found in a package sent from country B at the foreign mail
customs post office A. Similarly, a learning model AX is created
using image data of prohibited articles and restricted articles
actually found in a package sent from somewhere other than country
A and country B, at the foreign mail customs post office A. In this
way, by using learning models according to sender location, it is
possible to perform sorting giving consideration to trends in
packages sent from respective countries.
[0058] The determination part 103 in the present example embodiment
selects a learning model based on sender location information
obtained at the sender location recognition part 105, in addition
to a usage condition of the sorting support apparatus 100a inputted
by the operation terminal 180, and performs an inspection of the
package.
[0059] FIG. 9 is a flow chart representing operations of the
sorting support apparatus 100a in the present example embodiment.
Steps S011 and S012 are added to FIG. 5 that shows operations of
the first example embodiment. That is, the sorting support
apparatus 100a performs automatic recognition of sender location,
after input of an X-ray image (step S011). It is to be noted that
in the example of FIG. 9, automatic recognition of sender location
is performed after input of an X-ray image, but the automatic
recognition of the sender location may also be performed first.
[0060] The sorting support apparatus 100a performs reselection of a
learning model giving consideration to the recognized sender
location (step S012). Since subsequent operations are similar to
the first example embodiment, a description is omitted.
[0061] As described above, according to the present example
embodiment, it is possible to perform sorting support giving
consideration to change in packages according to difference of
sender location. It is to be noted that in the abovementioned
description, learning models are used according to sender location
in foreign mail customs post office units, as in foreign mail
customs post offices A and B, but it is also possible to employ a
configuration that does not consider differences of foreign mail
customs post offices, but provides learning models according to
sender location, to jointly use learning models at a plurality of
foreign mail customs post offices.
Fourth Exemplary Embodiment
[0062] Continuing, a description is given of a fourth example
embodiment combining the sorting support apparatuses of the first
to third example embodiments described above, to perform sorting of
packages in a stepwise manner. FIG. 10 is a diagram showing an
overall configuration of a system including a sorting support
apparatus in the fourth example embodiment of the disclosure. For
example, in a sorting support apparatus 100-1 at a first stage,
inspection of a package is performed using a learning model B
directed toward foreign mail customs post office B. In a sorting
support apparatus 100-2 at a second stage, inspection of a package
is performed using a learning model SP specialized for inspection
of prioritized inspection articles.
[0063] For example, as shown in FIG. 10, the sorting support
apparatus 100-1 uses the learning model B to inspect miscellaneous
packages (P1 to P3), and sorts a package (P3 in FIG. 10) that may
contain an article outside of paper. The sorting support apparatus
100-2 uses a learning model SP, inspects only a package (P3 in FIG.
10) that may contain an article outside of paper, and performs a
determination from a viewpoint as to whether or not it contains a
prioritized inspection article.
[0064] According to the fourth example embodiment with the above
type of multi-stage configuration, it is possible to focus on the
number of packages sent to the sorting support apparatus that
performs a more difficult inspection and determination, and to send
the package to a sorting support apparatus that uses a learning
model that performs a more sensitive inspection. This sensitive
inspection can realize changing of determination threshold even
where the same learning model is used. For example, in a
calculation using a learning model, in a case of a probability of
60% or more of a determination that a suspect article is contained,
instead of a normal-time operation determining that there is a
specific article, the threshold may be changed so as make a
determination that there is a specific article in a case where this
probability is 50% or more.
[0065] In the example of FIG. 10, a 2 stage configuration with a
sorting support apparatus 100-1 and a sorting support apparatus
100-2 is assumed, but a multi-stage inspection with 3 or more
stages may also be performed, using 3 or more sorting support
apparatuses.
[0066] From a different viewpoint, it is also possible to employ a
configuration in which a high risk article is detected first. For
example, sorting support apparatuses that perform inspection using
a learning model optimized for detection of "powder" and "pills"
that have a high probability of being drugs, may be disposed in a
first stage of multiple stages. From a similar viewpoint, sorting
support apparatuses that perform inspection using a learning model
optimized for detection of articles such as a "knife" or a "brand
name article" for which visual inspection is required, may be
disposed in any stage of multiple stages. In addition, low risk
articles outside of paper may be added to excluded targets in the
sorting support apparatus at a first stage.
[0067] A description has been given above of respective example
embodiments of the present disclosure, but the present disclosure
is not limited to the abovementioned example embodiments, and
modifications, substitutions and adjustments may be added within a
scope that does not depart from fundamental technical concepts of
the disclosure. For example, network configurations, respective
element configurations and message expression modes shown in the
respective drawings are examples for the purpose of aiding
understanding of the disclosure, and are not intended to limit the
disclosure to configurations illustrated in the drawings. In the
following description, "A and/or B" is used to indicate at least 1
of A or B.
[0068] For example, in each of the abovementioned example
embodiments a description has been given centered on using a
plurality of learning models in accordance with usage condition,
but it is also possible to change determination threshold in the
determination part 103 described above, in accordance with usage
condition. For example, in a case of using learning model A, which
assumes usage at foreign mail customs post office A, at foreign
mail customs post office A, a determination threshold of 50% is
assumed as a threshold for determining whether or not 1 or more
articles is included, but in a case of using learning model A at
other foreign mail customs post offices, it is also possible to
have another value as the determination threshold. Similarly, it is
also possible to change not only the learning model but also
determination threshold in accordance with season or sender
location, and to increase overall determination accuracy.
[0069] In the abovementioned respective example embodiments, a
description is given in which sorting support apparatuses 100,
100a, 100-1, 100-2 select 1 learning model, but these sorting
support apparatuses may select a plurality of learning models to
perform determination. For example, by creating a learning model
for each prohibited article, and the sorting support apparatus 100
performing an inspection of these prohibited articles in order, it
is possible to implement the inspection without compromising the
organization.
[0070] In the abovementioned respective example embodiments, a
description is given in which a usage condition of the sorting
support apparatus 100 is set by the operation terminal 180, but
modes in which the sorting support apparatus 100 specifies a usage
condition are not limited thereto. For example, instead of setting
by the abovementioned operation terminal 180, it is possible to
employ a mode in which a usage condition is set according to
transmission of a message or e-mail including information for
setting of usage condition to the sorting support apparatus 100.
For example, a configuration may also be employed in which the
sorting support apparatus 100 itself makes a query to a network or
information set therein, determines a usage condition indirectly
specifies by this information, and selects a learning model.
[0071] In the abovementioned respective example embodiments, a
description is given citing examples of using the sorting support
apparatus of the present disclosure in sorting a package at a
foreign mail customs post office, but usage of the sorting support
apparatus of the present disclosure is not limited to these
examples. By providing a required learning model, application is
also possible, for example, to inspection of packages at a
collection location of a national distributor or an airline.
[0072] For example, a lithium battery specified as an airline
hazardous substance may be enclosed with a small sized home
appliance, but according to the present disclosure, in a check when
an article is received at a shop or at an X-ray inspection at an
airport, by using a learning model optimized for detecting a
lithium battery from among a plurality of learning models,
detection can be carried out efficiently.
[0073] The procedure illustrated in the abovementioned first to
fourth example embodiments may be realized by a program that causes
a computer (9000 in FIG. 11) functioning as the sorting support
apparatus to realize functionality as a sorting support apparatus.
Such a computer is exemplified in a configuration provided with a
CPU (Central Processing Unit) 9010, a communication interface 9020,
a memory 9030, and an auxiliary storage apparatus 9040, in FIG. 11.
Namely, a determination program that uses a learning model or a
selection program for a learning model may be executed in the CPU
9010 of FIG. 11, and update processing of respective calculated
parameters held in the auxiliary storage apparatus 9040 may be
implemented.
[0074] That is, the respective parts (processing means, functions)
of the sorting support apparatus illustrated in the abovementioned
first to fourth example embodiments may be implemented by a
computer program that causes the abovementioned respective
processing to be executed in a processor installed in the sorting
support apparatus, using hardware thereof.
[0075] Finally, preferred modes of the present disclosure are
summarized.
<First Mode>
[0076] (Refer to the sorting support apparatus according to the
first aspect described above.)
<Second Mode>
[0077] In the sorting support apparatus, the learning model is
preferably created in accordance with a trend of handled goods at a
location where the sorting support apparatus is disposed.
<Third Mode>
[0078] In the sorting support apparatus, the learning model is
preferably created in accordance with a trend of handled goods at a
time-period when sorting is performed.
<Fourth Mode>
[0079] In the sorting support apparatus, the learning model is
preferably created in accordance with a trend of handled goods
according to sender location.
<Fifth Mode>
[0080] In the sorting support apparatus, it is preferable to be
able to change a threshold for determining, in the determination
part, whether or not the at least one article is included.
<Sixth Mode>
[0081] (Refer to the sorting support system according to the second
aspect described above.)
<Seventh Mode>
[0082] (Refer to the sorting support method according to the third
aspect described above.)
<Eighth Mode>
[0083] (Refer to the program according to the fourth aspect
described above.) It is to be noted that the abovementioned sixth
to eighth modes may be expanded with regard to the second to fifth
modes, similar to the first mode.
[0084] It is to be noted that the various disclosures of the
abovementioned Patent Literature and Non-Patent Literature are
incorporated herein by reference thereto. Modifications and
adjustments of example embodiments and examples may be made within
the bounds of the entire disclosure (including the scope of the
claims) of the present disclosure, and also based on fundamental
technological concepts thereof. Various combinations and selections
of various disclosed elements (including respective elements of the
respective claims, respective elements of the respective example
embodiments and examples, respective elements of the respective
drawings and the like) are possible within the scope of the
disclosure of the present disclosure. That is, the present
disclosure clearly includes every type of transformation and
modification that a person skilled in the art can realize according
to the entire disclosure including the scope of the claims and to
technological concepts thereof. In particular, with regard to
numerical ranges described in the present specification, arbitrary
numerical values and small ranges included in the relevant ranges
should be interpreted to be specifically described even where there
is no particular description thereof.
REFERENCE SIGNS LIST
[0085] 10 sorting support apparatus [0086] 11, 101 input part
[0087] 12, 102 storage part [0088] 13, 103 determination part
[0089] 100, 100a, 100-1, 100-2 sorting support apparatus [0090] 104
X-ray camera [0091] 105 sender location recognition part [0092] 170
rotating light [0093] 180 operation terminal [0094] 190 belt
conveyor [0095] 300, P1-P3 package [0096] 9000 computer [0097] 9010
CPU [0098] 9020 communication interface [0099] 9030 memory [0100]
9040 auxiliary storage apparatus
* * * * *