Vehicle detection in urban traffic: a systematic literature review
DOI:
https://doi.org/10.15381/risi.v17i2.29232Keywords:
Vehicle detection, urban traffic, systematic literature review, artificial intelligence, computer visionAbstract
Road safety is a highly relevant topic due to the high incidence of traffic accidents and their consequences. The main objective of this review article is to provide a comprehensive and updated overview of the advances in vehicle detection in urban traffic regarding the algorithms, data processing, and validation metrics used in this field. Exhaustive searches were carried out in Google Scholar, PubMed, and IEEE Xplore, in which studies published between 2019 and 2024 were chosen. The results revealed 34 selected investigations, with Google Scholar being the search engine with the largest number of studies, representing 47% of the total. The year 2022 registered the highest trend in publications, with a total of 5 studies. The most used algorithms were YOLOv5, Faster R-CNN, each representing 6 investigations. In addition, the use of proprietary data sets and advanced techniques for processing in complex urban traffic environments was observed. The most commonly used validation metric was accuracy, present in 16 investigations. In conclusion, this systematic review has a significant impact and establishes a solid foundation for future research in the field of road safety, effectively contributing to the development of this field.
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Copyright (c) 2024 Luz Elena Torres Talaverano, Ciro Rodriguez Rodriguez, Pedro Martin Lezama Gonzales, Ivan Carlo Petrlik Azabache
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