Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee, Munish
Bačić, Boris
Doborjeh, Maryam
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Journal Article
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Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.

4605 Data Management and Data Science , 46 Information and Computing Sciences , 3 Good Health and Well Being , 0301 Analytical Chemistry , 0502 Environmental Science and Management , 0602 Ecology , 0805 Distributed Computing , 0906 Electrical and Electronic Engineering , Analytical Chemistry , 3103 Ecology , 4008 Electrical engineering , 4009 Electronics, sensors and digital hardware , 4104 Environmental management , 4606 Distributed computing and systems software
Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 23(12), 5656-5656. doi: 10.3390/s23125656
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