Improved detection algorithm of tank and armored vehicles based on YOLOV3-tiny

Abstract

Target detection technology is a typical application in the military field. It can quickly and accurately find and identify all kinds of enemy vehicle targets in the battlefield, and respond to all kinds of battlefield targets more quickly, which has become the key to improve the battlefield situation. Because the battlefield environment is very complex, the traditional target detection algorithm is not ideal when detecting targets in complex scenes. Therefore, a target detection algorithm for armored vehicles of military tanks based on improved YOLOv3-tiny is proposed, which realizes the automatic detection of military targets in complex environments by deep learning. Firstly, based on YOLOv3-tiny algorithm, ResNext residual network is added to replace the original feature extraction network, which better improves the problem of missing and false detection of small targets and optimizes the convolution network structure. Then, the dense network is introduced, and the features of different layers are fused to realize feature reuse, which improves the efficiency of extracting better features of target vehicles, strengthens the network's ability to learn features, and improves the detection effect. Experimental results show that the recall rate and precision rate are increased by 4.62% and 3.79% respectively, the average precision rate is increased by 4.32%, and the frame rate can reach 78 frames/s.