Method for improving target detection performance by improving target classification and positioning accuracy

Abstract

The invention relates to a method for improving target detection performance by improving target classification and positioning accuracy. The method is characterized by extracting image characteristics according to a convolution neural network framework, selecting the first M layers of convolution layers to be output and carrying out characteristic fusion so as to form a multi-characteristic characteristic graph; carrying out grid division on the convolution layer M, and predicting target candidate frames with the fixed number and size in each grid; mapping the candidate frames to the characteristic graph for cutting, and carrying out multi-characteristic connection on cutting results; and passing the above results through a whole connection layer, classifying the image characteristics through the Softmax classification algorithm and using the overlapping area loss function to carry out online iteration regression positioning so as to obtain the final target detection result. According to the invention, the method is properly designed; characteristics are extracted through the convolution neural network, and multilayer fusion is performed on the image characteristics; and finally, the Softmax classification algorithm is used for classifying the image characteristics and the overlapping area loss function is used to carry out positioning, so a good target detection result is acquired.

Description

technical field [0001] The invention belongs to the technical field of target detection, in particular to a method for improving target detection performance by improving target classification and positioning accuracy. Background technique [0002] In the perception engineering of human beings in the material world, more than 80% of the information comes from vision. The image is a reflection of objective reality in a certain sense. It transmits information to human beings in different modes, and as an important information carrier, it has the characteristics of intuition, rich content and easy communication. Therefore, various applications based on image processing technology have emerged as the times require. Image target recognition and detection technology is one of the most typical application technologies. The purpose of computer vision research is to use computers to realize human perception, recognition and understanding of the objective world. Object Detection is ...

AI Technical Summary

Problems solved by technology

Although researchers have proposed many target detection algorithms based on deep learning convolutional neural networks, and these algorithms have achieved good results, there are still many aspects to be improved, such as complex picture background, fixed network input size, too many candidate frames, Problems such as slow training speed, computer memory consumption, inaccurate detection of small objects, cumbersome steps, and inaccurate positioning

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