Abstract
Vehicular object detection is the heart of any intelligent traffic system. It is essential for urban traffic management. Recent state-of-the-art methods apply R-CNN, Fast R-CNN, Faster R-CNN, and YOLO for this task. However, region-based CNN methods have the problem of higher inference time which makes them unrealistic to use the model in real-time. YOLO on the other hand struggles to detect small objects that appear in groups. In this paper, we propose a method that can locate and classify vehicular objects from a given densely crowded image using YOLOv5. We apply non-maximum suppression ensembling of 4 different models of YOLOv5 trained on different setups. The performance of our proposed model was measured on the Dhaka AI dataset which contains densely crowded vehicular images taken from both top view and side view of the street in both day and night settings. Our experiments show that our model achieved mAP@0.5 of 0.458 with an inference time of 0.75s outperforming other state-of-the-art models on performance. Hence, the model can be implemented in the street for real-world traffic detection which can be used for traffic control and data collection.
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Notes
- 1.
Available at:https://roboflow.com/.
- 2.
Available at https://github.com/ultralytics/yolov5.
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Acknowledgements
We would like to extend our gratitude to Mr. Redwan Karim Sony, Department of Computer Science and Engineering, Islamic University of Technology, and Mr. Mohammad Sabik Irbaz, Pioneer Alpha Limited for their continuous support and suggestions throughout the work. We would also like to thank the organizing committee of Dhaka AI 2020 for organizing the competition.
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Rahman, R., Bin Azad, Z., Bakhtiar Hasan, M. (2022). Densely-Populated Traffic Detection Using YOLOv5 and Non-maximum Suppression Ensembling. In: Arefin, M.S., Kaiser, M.S., Bandyopadhyay, A., Ahad, M.A.R., Ray, K. (eds) Proceedings of the International Conference on Big Data, IoT, and Machine Learning. Lecture Notes on Data Engineering and Communications Technologies, vol 95. Springer, Singapore. https://doi.org/10.1007/978-981-16-6636-0_43
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