Review of Typical Vehicle Detection Algorithms Based on Deep Learning
Gaoyan Dong, Bin Li, Yiliang Chen, Xinyu Wang · Journal of Engineering Research and Reports · 2022
Object detection is the crucial task in the field of computer vision. In recent years, intelligent driving technology and intelligent transportation system have set off a boom. Therefore, vehicle object detection has also become a hot research task in the field of computer vision and deep learning. With the rapid development of deep learning, the current mainstream vehicle detection algorithms are Convolutional Neural Networks (CNN)-based two-stage and one-stage object detection algorithms. Because of the local nature of the image presented by CNN, the global receptive field of the network is limited. At the same time, Transformer shows a strong long-distance dependence characteristic, and opens up a new idea of combining images with Transformer. Therefore, the research of object detection algorithm based on Transformer gradually causes a boom. This paper mainly introduces the advantages and disadvantages of several representative algorithm models, and makes a summary and prospect.