Target detection method based on information enhancement

Abstract

The invention discloses a target detection method based on information enhancement, and aims to overcome the defect of low precision of a single-stage detection method. According to the technical scheme, a target detection system composed of a feature extraction module, a semantic improvement module, a feature selection module, a feature fusion module and a detection module is constructed; a target detection network is trained by adopting the training data set; feature extraction, semantic improvement, feature selection and feature fusion are carried out on a single-frame image by adopting thetrained target detection system; and the position and category of the target are identified. The semantic enhancement module enriches semantic information of multi-scale features; the feature selection module enhances useful information in different scale feature maps by adopting an attention module, useless information is inhibited, and the purpose of information enhancement is achieved; the feature fusion module fuses the global semantic feature map subjected to feature selection to the multi-scale feature map, so that each feature map has more accurate position and semantic information, and the detection precision is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to an information enhancement-based target detection method. Background technique [0002] Target detection is one of the important research directions in the field of computer vision. The traditional target detection method is to extract features by constructing feature descriptors (such as histograms of orientation gradients, etc.) for images in a certain area, and then use classifiers to classify the features. Target detection, such as support vector machine SVM (Support Vector Machine), etc. Recently, with the development of convolutional neural networks, engineering features have mostly been replaced by convolutional neural network features, and object detection systems have made great progress in both accuracy and speed. [0003] Currently, object detection methods based on deep learning are divided into two-stage detection methods and single-stage detection methods. ...

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to solve the shortcomings of th

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