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Improved YOLOv5l for vehicle detection: an application to estimating traffic density and identifying over speeding vehicles on highway scenes

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Abstract

Vehicle detection and counting are getting progressively significant in highway administration due to the varying sizes of vehicles. This paper proposes a vision-based vehicle detection and counting framework. Initially, the COCO and BDD100K datasets are trained, employing improved YOLOv5l algorithm, using GhostBottleneck, for vehicle detection. Subsequently, the Centroid Tracking Algorithm counts the vehicles in videos uniquely. Later, vehicles are categorized as Light and Heavy vehicles. The model is evaluated over UCSD dataset for traffic estimation. Then, the speed of the recognized vehicle is computed to identify overspeeding. Lastly, number plate identification of the over speeding vehicles is performed. Vehicle detection performed well in precision, recall, F-score, and mAP. The centroid tracking algorithm had the best multiple object tracking accuracy and precision for vehicle tracking. The proposed model performed well in terms of detection rate, false alarm rate, and mean time to traffic detection.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The authors express their gratitude to Department of Science & Technology (DST), India for the obtained financial support in performing this research work. This work is one of the outcomes of the project entitled “IoT based real time and effective traffic signal scheduling for smart city” with sanction no. DST/ICPS/CPS-Individual/2018/678(G) dated 03.01.2019, sponsored by DST.

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Authors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, 211015, India

    Navjot Singh

  2. Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India

    Paras Saini, Om Shubham, Rituraj Awasthi & Anurag Bharti

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Neetesh Kumar

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Singh, N., Saini, P., Shubham, O. et al. Improved YOLOv5l for vehicle detection: an application to estimating traffic density and identifying over speeding vehicles on highway scenes. Multimed Tools Appl 83, 5277–5307 (2024). https://doi.org/10.1007/s11042-023-15520-9

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