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Anchor-frame-free target detection method and system based on diagonal network

A target detection and diagonal technology, applied in the field of computer vision, can solve the problems of low detection accuracy, unbalanced samples, and difficulty in anchor frame design, so as to improve the accuracy, improve the accuracy, and reduce the target false detection rate.

Pending Publication Date: 2022-08-05
中国人民解放军陆军炮兵防空兵学院
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is how to solve the technical problems of difficult anchor frame design, unbalanced samples and low detection accuracy in the prior art

Method used

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  • Anchor-frame-free target detection method and system based on diagonal network
  • Anchor-frame-free target detection method and system based on diagonal network
  • Anchor-frame-free target detection method and system based on diagonal network

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Experimental program
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Embodiment 1

[0073] like figure 1 and figure 2 As shown, the target detection framework of the present invention mainly includes the following modules:

[0074] Backbone and detection head module 1:

[0075] The present invention extracts the depth feature of the input image by using the Hourglass Net as the backbone network. Then, the depth features are subjected to Corner Pooling and Center Pooling respectively to obtain the following information:

[0076] Key point heat map (Heatmaps): including the upper left corner (Top-left), the lower right corner (Bottom-right) and the center point (Center) three types of key points. where the size of each heatmap is 4 times downsampling of the input image, denoted as W×H, each keypoint has a class label c∈{1,2,…,C}, and the heatmap dimension is 3×C ×W×H, each value on the heatmap represents the confidence that the keypoint appears at the corresponding location.

[0077] Embedding feature vector feature map (Embeddings): each key point corres...

Embodiment 2

[0086] The invention designs a diagonal network (DiagonalNet) for target detection without anchor frame for target detection. This scheme designs a grouped pairwise loss for supervised embedding vector learning; this scheme designs a diagonal loss for ensemble regression of predicted boxes. The proposed target detection method is experimentally verified on the MS COCO dataset, and compared with the target detection methods with anchor boxes (such as Faster R-CNN, SSD, etc.) and without anchor boxes (such as CenterNet, CornerNet, etc.) , the results show that the target detection method of the diagonal network proposed by the present invention has higher detection accuracy.

[0087] Table 1 Comparison of experimental results

[0088]

[0089] Among them, the corresponding Chinese names of the target detection algorithms in Table 1 are as follows: Faster R-CNN (faster R-CNN), TridentNet (Trident Network), SSD513 (single-step detector), RetinaNet (retina network), CornerNet (...

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Abstract

The invention provides a diagonal network-based anchor-frame-free target detection method and system. The method comprises the following steps of: extracting depth features of an input image by using an hourglass network and carrying out pooling operation so as to obtain a key point thermodynamic diagram, an embedded feature vector feature diagram and an offset feature diagram; processing the key point thermodynamic diagram by using a diagonal network, sorting the key points according to confidence coefficients, selecting the key points of the first k confidence coefficients as target key points, and obtaining key point positions and key point category information of the upper left corner, the lower right corner and the central point; based on a Fisher criterion, designing an embedded vector learning loss function to carry out key point combination pairing training, and further discriminating a preliminary pairing result by using a central key point; and using the distance, the length and the slope measurement loss of the diagonal center points of the prediction rectangle and the annotation rectangle frame to design the diagonal loss for regression training of the target prediction frame. The technical problems that the anchor frame is difficult to design, samples are unbalanced and the detection precision is low are solved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method and system for anchor-free frame target detection based on a diagonal network. Background technique [0002] Object detection is an important research direction in the field of computer vision, and its purpose is to predict the location and class of the bounding box of each object of interest in an image. Object detection has a wide range of applications in the fields of autonomous driving, video surveillance, visual guidance, and air defense early warning. At present, target detection based on deep learning mainly includes two categories: anchor-based target detection method and anchor-free target detection method. [0003] In the early stage of research, the target detection method based on anchor frame has received extensive attention, which has significantly improved the precision and recall rate of target detection, but it still has two problems: First, the layout of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/46
CPCG06V10/462G06V2201/07
Inventor 李从利秦晓燕刘永峰袁广林琚长瑞韦哲魏沛杰陈超逸
Owner 中国人民解放军陆军炮兵防空兵学院
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