Object detection method and moving object tracking method using it

Abstract

The present application proposes a target detection method, including: inputting the image to be detected into the backbone network to extract an initial feature map; inputting the initial feature map into the enhancement layer of the neck network to extract at least one enhanced feature map, wherein the neck network In each enhancement layer, the sequentially connected FPN structure, PAN structure, and target feature-specific attention module are set; the target feature-specific attention module includes a spatial attention branch and a feature-specific branch, and the spatial attention branch is combined with the feature The output of the specific branch is multiplied by element level and then normalized, and then multiplied by element level and added by element level with the corresponding fusion feature map to obtain the corresponding enhanced feature map; input each enhanced feature map to the header internal prediction network to obtain prediction results. In this method, the target feature-specific attention module can effectively perceive the spatial areas occupied by different types of objects through the combined use of two branches, and improve the accuracy and efficiency of target detection.

Description

technical field [0001] The present application relates to the technical field of target detection, in particular to a target detection method and a moving target tracking method using the same. Background technique [0002] Computer vision has gradually become more and more important in people's lives. It is widely used in robot navigation, intelligent video surveillance, industrial inspection, aerospace and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision; Detection and target tracking is a popular direction of computer vision and digital image processing. It is an important branch of computer vision and image processing, and it is also the core part of the intelligent monitoring system; due to the further development of the combination of artificial intelligence and cameras, the intelligent monitoring system And slowly began to come into everyone's sight. [0003] Although some progress has been ...

AI Technical Summary

Problems solved by technology

[0003] Although some progress has been made in the field of object detection, there are still many problems in the actual design and use of object detection methods and object detection models.

On the one hand, in the attention module in the target detection model, the convolutional neural network will accumulate many convolutional layers and pooling layers to obtain contextual semantic information, which increases the complexity of time and space, and the existing spatial attention module Or channel attention will ignore the underlying features in the image to be detected. In particular, common spatial attention modules and channel attention modules cannot effectively perceive the spatial regions occupied by different types of objects.

On the other hand, in single-stage object detection, it is often achieved by optimizing object classification and object localization, but conventional object classification and object localization are parallel, which may lead to a certain degree of spatial misalignment.

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