Multi-scale target detection method based on self-attention mechanism

A target detection and attention technology, which is applied in the field of image processing, can solve the problems of high missed detection rate and low detection accuracy, achieves a small amount of calculation, takes into account detection accuracy and speed, and enhances the ability to express and capture contextual information. Effect

Active Publication Date: 2019-12-03
CHANGAN UNIV
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Problems solved by technology

[0005] In view of the above defects, the present invention provides a multi-scale target detection method based on the self-attention mechanism. The multi-scale feature fusion based on the self-attention mechanism in the present invention can make full use of the context infor

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  • Multi-scale target detection method based on self-attention mechanism
  • Multi-scale target detection method based on self-attention mechanism
  • Multi-scale target detection method based on self-attention mechanism

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Embodiment Construction

[0044] Embodiments of the present invention will be described in detail below in conjunction with examples, but those skilled in the art will understand that the following examples are only for illustrating the present invention, and should not be regarded as limiting the scope of the present invention.

[0045] The embodiment of the present invention provides a multi-scale target detection method based on the self-attention mechanism. Its network structure is similar to the traditional SSD, and the structure is as follows: figure 1 As shown, the method includes the following steps:

[0046] Step 1. Obtain the training sample set, select the basic network, construct a multi-scale feature pyramid, and use it as a feature extraction network to extract the convolutional feature map of the image to be detected;

[0047] It is specifically implemented according to the following steps:

[0048] Step 1.1, obtain the original training samples, flip, cut, compress or stretch the origi...

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Abstract

The invention discloses a multi-scale target detection method based on a self-attention mechanism. By adopting a bottom-to-top and top-to-bottom multi-scale feature fusion mode based on a self-attention feature selection module, low-level features and high-level features of a target can be combined, the representation capability of a feature map and the capability of capturing context informationare enhanced, and the stability and robustness of a target detection stage are improved. Moreover, the self-attention module is used for re-calibrating the features, the calculated amount is smaller,the detection precision and speed are both considered, and the method has important significance for solving the detection problems of dense objects, small targets, shielded targets and the like in target detection.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multi-scale target detection method based on a self-attention mechanism. Background technique [0002] Target detection is a basic problem in the field of computer vision. The purpose is to find objects of interest in images or videos, and determine their category, location and size. There are important researches in the fields of pedestrian detection, safety inspection and unmanned driving. and application value. With the rapid development of deep convolutional networks, the accuracy of object detection algorithms continues to improve. The current mainstream target detection algorithms are mainly divided into two categories: the detection algorithm based on the region proposal (Region Proposal) and the detection algorithm based on the bounding box regression. [0003] The idea of ​​the target detection algorithm based on candidate regions is to first use...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06T7/00
CPCG06T7/0002G06T2207/20016G06T2207/20081G06T2207/20084G06N3/045G06F18/214
Inventor 任卫军丁国栋王茹侯晓波葛瑶
Owner CHANGAN UNIV
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