A review of vehicle detection methods based on computer vision

作     者：Changxi Ma Fansong Xue 

作者机构：School of Traffic and TransportationLanzhou Jiaotong UniversityLanzhou 730070China Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and ControlLanzhou 730070China 

出 版 物：《Journal of Intelligent and Connected Vehicles》 (智能网联汽车（英文）)

年 卷 期：2024年第7卷第1期

页      面：1-18页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(No.52062027) the Key Research and Development Project of Gansu Province(No.22YF7GA142) the Soft Science Special Project of Gansu Basic Research Plan(No.22JR4ZA035) the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(Nos.22ZD6GA010 and 21ZD3GA002) the Natural Science Foundation of Gansu Province(No.22JR5RA343) 

主　　题：intelligent transportation system computer vision deep learning vehicle detection object detection algorithm 

摘      要：With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation *** order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant *** study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection *** the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network ***,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation *** to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and *** single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection *** the follow-up research,it is important to consider the balance between detection efficiency and detection ***,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into *** will ensure better application of the research findings in engineering practice.