@Article{cmes.2024.054735,
AUTHOR = {Carmen Gheorghe, Mihai Duguleana, Razvan Gabriel Boboc, Cristian Cezar Postelnicu},
TITLE = {Analyzing Real-Time Object Detection with YOLO Algorithm in Automotive Applications: A Review},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {141},
YEAR = {2024},
NUMBER = {3},
PAGES = {1939--1981},
URL = {http://www.techscience.com/CMES/v141n3/58495},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2024.054735}
}