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.
