Target detection method based on SSA sharpening attention mechanism

A target detection and attention mechanism technology, applied in the field of computer vision, can solve problems such as loss of edges of large targets and difficulty in locating small and medium targets

Active Publication Date: 2021-09-14
HEFEI LONGTUTEM INFORMATION TECH CO LTD
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Problems solved by technology

[0005] The present invention proposes a target detection method based on the SSA sharpening attention mechanism in order to solve the problem

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  • Target detection method based on SSA sharpening attention mechanism
  • Target detection method based on SSA sharpening attention mechanism
  • Target detection method based on SSA sharpening attention mechanism

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[0034] The invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Step (1): input image size is 640 × 640; a first-order target detection network lightweight YOLOv5s;

[0036] Step (2): The SSA spatial sharpening module and SE, ECA channel attention module combination, and attention divided into two modules: the channel and mixing attention module attention module, such as figure 2The channel attention is shown; all of the large residual blocks of the skeleton network of a stage of lightweight neural networks and the class FPN structure; channel attention modules are connected in series SSA space sharpening modules and embedded to a stable lightweight The node of the neural network output section, if the neural network includes the lower sampling structure, the next sampling node is embedded, here is a shallow feature 1 Take an example, set i 1 For the input characteristics of the hybrid focus module, the ...

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Abstract

The invention discloses a target detection method based on an SSA sharpening attention mechanism, in the method, user-defined sharpening filtering is introdued into a space attention module for the first time, and combined with a channel attention module for use, thereby reducing the influence of interference factors on the SSA sharpening effect, and serving the sharpening effect; according to the invention, the edge information of the detected object in the neural network is enhanced spatially, and object positioning is enhanced. Edge information of large objects can be perfected, existence of small and medium objects in an output layer can be improved, and the detection effect is improved; according to the invention, the combination mode and the embedding position of the SSA space sharpening module and the channel attention module are perfected. Compared with a space attention module in the CBAM, the effect on the lightweight target detection model is better. According to the SSA space sharpening module, the required calculation amount and parameter amount are extremely small, the detection speed is hardly influenced, and the lightweight module is high in practicability, plug-and-play and easy to implement.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a lightweight spatial attention module based on a neural network and a sharpening attention mechanism and a target detection method combined with a channel attention module on a one-stage lightweight neural network. Background technique [0002] Document 1 (Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018.7132-7141) introduces channel attention after the network intermediate feature map The channel information of the feature map is extracted through the average pooling operation, and the global features at the channel level are obtained. Then, the connection between each channel is learned through the FC multi-layer perceptron with a hidden layer, and the weights of different channels are also obtained. After the limit of the output is limited by the Si...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08G06T5/00
CPCG06N3/08G06T5/003G06T2207/10004G06N3/045
Inventor 薛梦凡陈明皓彭冬亮杨岗贾士绅陈怡达
Owner HEFEI LONGTUTEM INFORMATION TECH CO LTD
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