Target detection based on improved Yolov4-Tiny algorithm

Abstract

At present, the development of artificial intelligence is very rapid, and the intelligent assisted driving system based on deep learning is widely used in the society. For example, in unmanned driving, it can accurately identify pedestrians, vehicles and traffic signs. Convolutional neural network in deep learning has excellent achievements in the field of computer vision and has outstanding feature extraction ability. Therefore, object detection algorithm based on deep learning is a research hotspot in the field of computer vision at present. We propose a vehicle-pedestrian target detection method based on Yolov4-tiny. Firstly, the ResBlock-D module in the ResNet-D network is used to replace one CSPBlock module in Yolov4- tiny, thus reducing the computational complexity. Then, the coordinate attention mechanism is added to help the model better locate and identify targets. Experimental results show that The improved Yolov4-tiny algorithm has higher curacy than the original algorithm, and the Map is improved by 7.8 %, which has a certain reference value for the study of intelligent assisted driving technology.