A Target Detection Method Based on Information Enhancement

Abstract

The invention discloses a target detection method based on information enhancement, aiming at solving the shortcoming of low precision of the single-stage detection method. The technical solution is to construct 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, use the training data set to train the target detection network, and use the trained target detection system to detect a single frame. The image is subjected to feature extraction, semantic enhancement, feature selection, and feature fusion to identify the location and category of the target. The semantic enhancement module of the present invention enriches the semantic information of multi-scale features, and the feature selection module adopts the attention module to enhance the useful information and suppress the useless information in the feature maps of different scales, so as to achieve the purpose of enhancing information; The semantic feature maps are fused to the multi-scale feature maps, so that each feature map has more accurate location and semantic information, which improves the detection accuracy.

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 Recently, with the development of convolutional neural networks, engineering features have mostly been replaced by convolutional neural network features, and object detection systems have made great progress in both accuracy and speed.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. ...

AI Technical Summary

Problems solved by technology

[0008] The technical problem to be solved by the present invention is to solve the shortcomings of the current single-stage detection method, although the detection speed is fast, but the accuracy is low

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