Object detection method based on cascade convolution neural network

Abstract

The invention relates to an object detection method based on a cascade convolution neural network. The object detection method based on the cascade convolution neural network comprises steps of usinga convolution neural network to extract image characteristics, using a regional candidate network to generate a certain quantity of object candidate frames, using an optimized network to optimize a candidate framework, inputting the optimized object candidate frames into a detection network containing multiple classifiers to produce a preliminary detection result, using a binary classifier to perform re-detection on each kind of objects, and removing an error target to obtain a final accurate detection result. The object detection method based on the cascade convolution neural network uses anadvantage that the deep convolution network has a strong expression ability for a target, constructs a cascade convolution neural network for target detection, brings forward a new method of optimizing the target candidate frame and a strategy of eliminating an error detection sample, improves algorithm detection accuracy and can obtain a better target detection result.

Description

technical field [0001] The invention belongs to the technical field of visual target detection, in particular to a target detection method based on a cascaded convolutional neural network. Background technique [0002] More than 80% of the information that humans perceive every day comes from vision. As an important part of multimedia, images carry intuitive and rich information, so image processing technology is an important part of multimedia technology. Computer vision refers to the use of cameras and computers instead of human eyes to identify, track and measure targets, and further process them into images that are more suitable for human eyes to observe or send to instruments for detection. As one of the important research topics of computer vision, object detection is widely used in various fields such as video surveillance, automatic driving, augmented reality and intelligent interaction, and has broad application prospects. [0003] Target detection technology is ...

AI Technical Summary

Problems solved by technology

However, the target detection model of a single network is still inferior to the target detection model based on the region candidate frame in terms of detection accuracy.

[0005] Although the target detection algorithm has achieved good results after decades of development, and the appearance of the convolutional neural network has improved the target detection accuracy a lot, there are still problems such as how to improve the recall rate of the region candidate frame and the target positioning error. Large, background interference and other issues

Images