Small target detection method based on multilevel residual network perception and attention mechanism

Abstract

The invention discloses a small target detection method based on multilevel residual network perception and an attention mechanism. A single-stage detector YOLOv5 is adopted in Baseline; the method comprises the following specific steps: (1) constructing a virtual training sample, expanding the scale of a training data set, and improving the overall performance of a model; (2) extracting multi-dimensional features of the image by using multi-layer residual convolution; (3) an attention mechanism is used for enhancing a superficial layer feature map; (4) connecting the feature pyramid with the shallow feature map; (5) using a loss function optimization model to predict target position information, category information and confidence; and (6) the P2 detection layer is matched with Lufl and VIoU Loss to predict a small target. According to the method, the data set is enhanced and expanded through selective samples, the difference between the samples is balanced, the shallow feature map is fully utilized, and the recall rate and the accuracy rate of small target detection are remarkably improved.

Description

technical field [0001] The invention relates to the field of target detection and recognition, and relates to a small target detection method based on a multi-level residual network perception and attention mechanism. Background technique [0002] At present, different scenarios have different definitions for small goals, and a unified standard has not yet been formed. The existing mainstream small target definition methods are mainly divided into the following two categories, namely, the definition based on relative scale and the definition based on absolute scale. Relative scale is generally defined as the ratio of the bounding box area to the image area with a median between 0.08% and 0.58%. Absolute scale is generally defined as an object with a resolution less than 32 pixels by 32 pixels. The design of existing algorithms tends to focus more on the detection performance of large and medium-scale objects. There are not many optimization designs for the characteristics...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a small target detection method based on a multi-level residual network perception and attention mechanism, which solves the problem of missed detection of small target objects, false detection, low resolution, few available features, and few training samples in traditional target detection methods , little contribution to the loss function, difficult positioning and many other problems

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