Open Access

REVIEW

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

Carmen Gheorghe^*, Mihai Duguleana, Razvan Gabriel Boboc, Cristian Cezar Postelnicu

Department of Automotive and Transport Engineering, Transilvania University of Brasov, Brasov, 500036, Romania

* Corresponding Author: Carmen Gheorghe. Email:

Computer Modeling in Engineering & Sciences 2024, 141(3), 1939-1981. https://doi.org/10.32604/cmes.2024.054735

Received 06 June 2024; Accepted 05 September 2024; Issue published 31 October 2024

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.

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Keywords

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