DETECTION METHOD AND DETECTION DEVICE

• US 20200134313A1
• Filed: 10/22/2019
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Endoh, Toshio, Kato, Keizo
• Granted Patent: US 11,210,513 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/421 by analysing segments intersecting the pattern
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/774 Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 20/00 Scenes; Scene-specific elements control of digital cameras H04N23/60
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition

Abstract

A computer-implemented detection method includes, in response to inputting a first image including a region of one or more objects to a learned model, identifying a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object, detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects, and determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

Claims

1. A computer-implemented detection method comprising:

in response to inputting a first image including a region of one or more objects to a learned model, identifying a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object;

detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects; and

determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

2. The detection method according to claim 1, wherein

the identifying of the first entire image includes identifying a plurality of entire images corresponding to respective entirety of a plurality of target objects including the first target object, and

the detecting of the existing region includes selecting the first target object based on a difference between each of the plurality of entire images and the region of the one or more target objects.

3. The detection method according to claim 2, wherein

the determining includes detecting a second target object among the plurality of target objects based on a difference between each of the plurality of entire images other than the first entire image and the region of the one or more target objects other than the existing region.

4. The detection method according to claim, wherein

the detecting of the existing region includes determining the existing region based on a size of a difference between the first entire image and the region of the one or more target objects.

5. A detection device comprising;

a memory; and

a processor coupled to the memory and the processor configured to;

in response to inputting a first image including a region of one or more objects to a learned model, perform identification of a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object,

perform detection of an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects, and

perform, based on a specific image obtained by invalidating the existing region in the first image, determination of whether another target object is included in the first image.

6. The detection device according to claim 5, wherein

the identification of the first entire image includes identifying a plurality of entire images corresponding to respective entirety of a plurality of target objects including the first target object, and

the detection of the existing region includes selecting the first target object based on a difference between each of the plurality of entire images and the region of the one or more target objects.

7. The detection device according to claim 6, wherein

the determination includes detecting a second target object among the plurality of target objects based on a difference between each of the plurality of entire images other than the first entire image and the region of the one or more target objects other than the existing region.

8. The detection device according to claim 5, wherein

the detection of the existing region includes determining the existing region based on a size of a difference between the first entire image and the region of the one or more target objects.

9. A non-transitory computer-readable medium storing instructions executable by one or more computers, the instructions comprising:

one or more instructions for identifying a first entire image corresponding to entirety of a first object as a detection candidate in response to inputting a first image including a region of one or more objects to a learned model, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object;

one or more instructions for detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects; and

one or more instructions for determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

10. The medium according to claim 9, wherein

the identifying of the first entire image includes identifying a plurality of entire images corresponding to respective entirety of a plurality of target objects including the first target object, and

the detecting of the existing region includes selecting the first target object based on a difference between each of the plurality of entire images and the region of the one or more target objects.

11. The medium according to claim 10, wherein

the determining includes detecting a second target object among the plurality of target objects based on a difference between each of the plurality of entire images other than the first entire image and the region of the one or more target objects other than the existing region.

12. The medium according to claim 9, wherein

the detecting of the existing region includes determining the existing region based on a size of a difference between the first entire image and the region of the one or more target objects.