Target detection method and system based on multi-scale feature fusion in image

Abstract

The invention discloses a target detection method and system based on multi-scale feature fusion in an image. The method comprises the steps that firstly, zooming of different scales is carried out byutilizing the to-be-detected picture, and an image pyramid is constructed; secondly, a set of multi-scale detection templates covering most of the sample dimensions is obtained by utilizing a statistical clustering method; thirdly, based on the multi-scale detection template, scale self-adaptive target context construction is performed; fourthly, multi-scale depth features are fused; fifthly, thenon-maximum value restrain based on soft judgment is carried out. The invention is advantageous in that the image multi-resolution sparse pyramid, the multi-scale detection template and the templatescale self-adaptive context, multi-scale depth feature fusion and the like are constructed, full mining and fusion utilization of the depth features are achieved, and the target detection performancecan be improved.

Description

technical field [0001] The invention relates to a method in the field of target detection in images, in particular to a target detection method and system for multi-feature fusion in images. Background technique [0002] Object detection and recognition in images has a wide range of practical needs in applications such as intelligent video surveillance, and it is also a popular research direction in the field of computer vision. Due to the following difficulties and challenges in the existing image target detection methods, the detection results still need to be improved: (1) There are great diversity and differences in the appearance features such as color, texture and shape among similar targets. (2) There is a diversity of poses in the same type of target, resulting in a large change in the structural characteristics of the samples within the class. For example, in reality, the target has postures such as upright, lying down, and tilting, and similar targets with differe...

AI Technical Summary

Problems solved by technology

Due to the following difficulties and challenges in the existing image target detection methods, the detection results still need to be improved: (1) There are large diversity and differences in the appearance features such as color, texture and shape among similar targets

(2) There is a diversity of poses in similar targets, resulting in large changes in the structural characteristics of samples within the class

(4) The occlusion of the target will affect the detection results

After the target is occluded, some of its information is missing, which increases the difficulty of detection

(5) The diversity of the target's environmental background and illumination leads to an increase in false detections

Most of these target detections based on deep learning technology are based on single-scale, fixed-size context depth features, and there is still the problem of insufficient utilization of deep features, and the detection performance needs to be further improved

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