Target detection network and method based on mixed cavity convolution pyramid

A technology of target detection and pyramid, which is applied in the field of target detection network based on hybrid hole convolution pyramid, can solve the problems of affecting detection results, redundant feature map fusion methods, and poor backbone network performance.

Active Publication Date: 2021-09-14
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

This method uses feature maps of different sizes, but the feature map fusion method is relatively redundant, and the performance of the backbone network is not good, which affects the final detection results

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  • Target detection network and method based on mixed cavity convolution pyramid
  • Target detection network and method based on mixed cavity convolution pyramid
  • Target detection network and method based on mixed cavity convolution pyramid

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Embodiment 1

[0051] As the basic implementation of the present invention, the present invention includes a target detection network based on a hybrid dilated convolutional pyramid, including a backbone network, a mixed receptive field module, a low-level embedded feature pyramid module, and a detection module. The backbone network uses a hierarchical and cascaded network structure to extract target picture features; the mixed receptive field module is used to enhance the features of the highest-level feature map output from the top of the backbone network. The low-level embedded feature pyramid module is used to fuse high-level features downward on the basis of the feature pyramid, and generate a final feature map to be detected by means of low-level embedding. The detection module is used to locate and classify the feature map to be detected, and output the result.

[0052] The backbone network can be a single-stage detection network based on the Res2Net50 network, which has a stronger fe...

Embodiment 2

[0059] As the best implementation mode of the present invention, the present invention includes a target detection network based on a hybrid dilated convolutional pyramid, referring to the appended figure 1 , including a backbone network, a mixed receptive field module, a low-level embedded feature pyramid module, and a detection module.

[0060] The backbone network adopts a single-stage detection network structure, introduces the Achor-free mechanism of FCOS, performs pixel-by-pixel prediction, does not rely on pre-defined anchor frames or proposed regions for target detection, and reduces the invalidity caused by redundant candidate frames Calculation improves the positioning accuracy and effectively solves problems such as missed detection. It uses its Centerness mechanism to quickly filter negative samples, suppress low-quality prediction frames far from the target center, and increase the weight of the prediction frame close to the target center. Detection performance. ...

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Abstract

The invention relates to the technical field of digital image processing, in particular to a target detection network and method based on a hybrid cavity convolution pyramid, and the target detection network comprises a backbone network, a hybrid receptive field module, a low-layer embedded feature pyramid module and a detection module. The backbone network uses a hierarchical cascade network structure to extract target picture features; the hybrid receptive field module performs feature enhancement on the highest layer feature map output by the topmost end of the backbone network; the low-layer embedded feature pyramid module is used for downwards fusing high-layer features on the basis of a feature pyramid and generating a final to-be-detected feature map in a low-layer embedding manner; and the detection module is used for positioning and classifying the to-be-detected feature map and outputting a result. Through the target detection network and the target detection method, the problems of missing detection and error detection caused by scales and shielding can be effectively solved.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, in particular to a target detection network and method based on a mixed hole convolution pyramid. Background technique [0002] Object detection is one of the most widespread applications in real life, where the task is to focus on a specific object in a picture. The traditional target detection method can be divided into a single-stage target detection method and a two-stage target detection method. The core of the two-stage method is to use the region proposal method to selectively search the input image and generate a region suggestion box, and then propose a region for each region. The box uses a convolutional neural network to extract features, and then uses a classifier for classification. The single-stage method directly outputs the target detection results through the convolutional neural network. [0003] After a series of variants, the common point of these tw...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/62
CPCG06N3/08G06N3/045G06F18/253
Inventor 殷光强殷康宁候少麒梁杰丁一寅曾宇昊
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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