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research-article

Object Detection Using Deep Learning Methods in Traffic Scenarios

Authors:

Azzedine Boukerche,

Zhijun HouAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 2

Article No.: 30, Pages 1 - 35

https://doi.org/10.1145/3434398

Published: 05 March 2021 Publication History

Abstract

The recent boom of autonomous driving nowadays has made object detection in traffic scenes a hot topic of research. Designed to classify and locate instances in the image, this is a basic but challenging task in the computer vision field. With its powerful feature extraction abilities, which are vital for object detection, deep learning has expanded its application areas to this field during the past several years and thus achieved breakthroughs. However, even with such powerful approaches, traffic scenarios have their own specific challenges, such as real-time detection, changeable weather, and complex lighting conditions. This survey is dedicated to summarizing research and papers on applying deep learning to the transportation environment in recent years. More than 100 research papers are covered, and different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. Some open research fields are also provided. We believe that it is the first survey focusing on deep learning-based object detection in traffic scenario.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 2

March 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3450359

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Albert Zomaya
University of Sydney, Australia

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Published: 05 March 2021

Accepted: 01 November 2020

Revised: 01 September 2020

Received: 01 September 2019

Published in CSUR Volume 54, Issue 2

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https://doi.org/10.1142/S0218001424590195
Dolatyabi PRegan JKhodayar M(2025)Deep Learning for Traffic Scene Understanding: A ReviewIEEE Access10.1109/ACCESS.2025.352928913(13187-13237)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3529289
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