Abstract
As the volume of vehicles on our roads continues to surge, accurate detection and counting of vehicles have become critical for effective traffic management. Identifying vehicles precisely is challenging due to the wide range of sizes, shapes, and external factors influencing computer vision. To overcome these challenges, here propose a vehicle detection strategy based on the YOLOv5 algorithm. YOLOv5 is an advanced object detection algorithm leveraging convolutional neural networks (CNNs) for high-precision, high-speed detection in images and videos. Our strategy harnesses YOLOv5’s capabilities, optimizing it for both speed and accuracy. Comprising convolutional layers, pooling layers, and fully connected layers, YOLOv5 collaboratively detects and identifies vehicles in images or video frames. Extensive training on a diverse dataset empowers the algorithm to recognize vehicles with exceptional precision. An empirical study evaluated YOLOv5’s performance across diverse vehicle types and environmental conditions. Results unequivocally demonstrated substantial improvements in vehicle detection speed and precision. Even under challenging scenarios, the algorithm consistently achieved real-time identification and enumeration of vehicles.
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Patel, P., Mav, R., Mehta, P., Mer, K., Kanani, J. (2024). Improving Traffic Surveillance with Deep Learning Powered Vehicle Detection, Identification, and Recognition. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_9
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