Research based on improved SSD target detection algorithm

Abstract

In view of the problem of missed detection and false detection in complex environment, especially at night environment and false target environment, the detection ability of target detection is poor. With the development of deep learning, an improved SSD-based target detection algorithm is proposed, and the attention mechanism and function fusion module are added on the basis of SSD, which is integrated into the original network. Secondly, FPN module is a kind of shallow network, which is used to integrate deep network and shallow network to improve the representation ability of semantic information. Experiments were carried out on VOC2007 data set, pseudo target detection data set and night target detection data set. The results show that the detection accuracy of this method is up to 92.1%, which is verified by the camouflage data set and the night target detection data set. Compared with SSD andMobile-V2-SSD, the detection accuracy of this method is improved by 16.3% and 4.8%, respectively, and it has better robustness and real-time detection ability in complex environments.