Analyzing Real-Time Object Detection with YOLO Algorithm in Automotive Applications:A Review  

作　　者：Carmen Gheorghe Mihai Duguleana Razvan Gabriel Boboc Cristian Cezar Postelnicu 

机构地区：[1]Department of Automotive and Transport Engineering,Transilvania University of Brasov,Brasov,500036,Romania

出　　处：《Computer Modeling in Engineering & Sciences》2024年第12期1939-1981,共43页工程与科学中的计算机建模（英文）

摘　　要：Identifying objects in real-time is a technology that is developing rapidly and has a huge potential for expansion in many technical fields.Currently,systems that use image processing to detect objects are based on the information from a single frame.A video camera positioned in the analyzed area captures the image,monitoring in detail the changes that occur between frames.The You Only Look Once(YOLO)algorithm is a model for detecting objects in images,that is currently known for the accuracy of the data obtained and the fast-working speed.This study proposes a comprehensive literature review of YOLO research,as well as a bibliometric analysis to map the trends in the automotive field from 2020 to 2024.Object detection applications using YOLO were categorized into three primary domains:road traffic,autonomous vehicle development,and industrial settings.A detailed analysis was conducted for each domain,providing quantitative insights into existing implementations.Among the various YOLO architectures evaluated(v2–v8,H,X,R,C),YOLO v8 demonstrated superior performance with a mean Average Precision(mAP)of 0.99.

关 键 词：YOLO AUTOMOTIVE autonomous vehicles TRAFFIC INDUSTRY 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]