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A Weakly Supervised Object Detection Method Based on Pseudo-truth Adaptive Method

A technology of object detection and adaptive method, which is applied in neural learning methods, biological neural network models, image analysis, etc., and can solve problems such as inaccurate object position detection and inconsistent categories

Active Publication Date: 2020-09-22
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the existing full-supervised object detector needs to rely on a large number of databases with labeled information, and at the same time solve the problem of inaccurate object position detection due to labeling errors when the picture contains multiple objects and the objects are occluded from each other, and the actual The objects that need to be detected in the application may not match the object categories of the database or far exceed the shortcomings of the categories in these databases, and a weakly supervised object detection method based on the pseudo-truth adaptive method is proposed, including:

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specific Embodiment approach 1

[0023] Specific implementation mode 1: The weakly supervised object detection method based on the false truth adaptive method of this implementation mode includes:

[0024] Step 1), constructing training samples;

[0025] Step 2), input the picture in the training sample into the weakly supervised object detector based on multiple-instance learning method (Multiple-Instance Learning);

[0026] Step 3), the output result of the weakly supervised object detector is subjected to non-maximum suppression processing, and the bounding box with the highest score of each object is selected in the processing result picture;

[0027] Step 4), according to the position information of the selected bounding box in step 3), train the candidate region generation network, use the candidate region generation network to generate a plurality of candidate regions, and keep all candidate regions whose overlapping area ratio with the true value is greater than a certain threshold; Objects of each c...

specific Embodiment approach 2

[0035] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is that step one specifically includes:

[0036] Step 1.1), receiving keywords input by the user; the keywords are used to indicate the category of the object;

[0037] Step 1.2), use the keyword to search in the search engine, select a preset number of search results and use the keyword as the annotation information of the search result.

[0038] That is, the present invention only needs to know the simple object category information in the picture, and can train the model without complex object position information. The simple object category information here can be obtained in many ways, such as searching for pictures in the search engine in the form of keywords ("pedestrians", "vehicles", etc.), and downloading the top few thousand pictures can be used as training samples , no manual labeling is required.

[0039] It can be understood that, when u...

specific Embodiment approach 3

[0041] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in step 1), the training sample set can be any one of PASCAL VOC 2007 / 2012, MC COCO, WIDER FACE and FDDB database, or is A database constructed according to the method of Embodiment 2. The above English names are the names of the databases.

[0042] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention relates to a weakly supervised object detection method based on the false truth self-adaptive method. It is proposed due to the shortcomings of inaccurate object position detection during occlusion, including: inputting the picture into a weakly supervised object detector, performing non-maximum suppression processing on the output result of the detector, and selecting the boundary with the highest score for each object in the processing result box; train the candidate area generation network according to the position information of the selected bounding box, and retain the bounding box whose overlapping area with the bounding box with the highest score is greater than a certain value, and average the pixel coordinates of the candidate area corresponding to the same object, A unique bounding box for each object is determined from the computation; the bounding box information is fed into a fully supervised object detector as a pseudo-truth. The invention is suitable for object detection technology, especially general object detection technology in real scenes.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a weakly supervised object detection method based on a pseudo-truth adaptive method. Background technique [0002] Object detection is a very important research topic in the field of machine vision. It is the basic technology for advanced tasks such as image segmentation, object tracking, and behavior analysis and recognition. In addition, with the development of mobile Internet technology, the number of images and videos is increasing in an explosive manner. There is an urgent need for a technology that can quickly and accurately identify and locate objects in images and videos, so as to intelligently classify subsequent images and videos and identify key information. Obtain. Now object detection technology is widely used in modern society, such as face detection and pedestrian detection in the security field, traffic sign recognition in intelligent transportation, vehicle detecti...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/08
CPCG06N3/084G06T7/0002G06T2207/20081G06F18/24
Inventor 张永强丁明理李贤杨光磊董娜
Owner HARBIN INST OF TECH
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