Abstract
In order to implement safety measures on Indian roads, it is imperative to identify traffic rule violators. However, this is a difficult task due to a number of difficulties, such as occlusion and illumination. In this study, we provide a complete system for traffic law enforcement that includes detection of violations, notification of offenders, and storage of violations for statistical analysis and generating. The suggested method starts by employing object detection, which is done with YOLOv5, to identify bikes. Then, each motorcycle is appropriately evaluated for the applicable offenses, such as not wearing a helmet, triple riding, signal jum**, and not parking. Traffic regulation violations are detected using YOLOv5 and a classifier built on a convolutional neural network (CNN). Following offenses, vehicle numbers are recorded.
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Lavanya, T.S., Suneetha, K.R. (2023). Automatic Traffic Rule Violations Detection Using Deep Learning Techniques. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2022). ICTCS 2022. Lecture Notes in Networks and Systems, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-19-9638-2_7
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DOI: https://doi.org/10.1007/978-981-19-9638-2_7
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