Identification of traffic rule offenders is a highly desirable but tough task due to several challenges including occlusion, illumination, etc., which must be overcome in order to provide safety measures on Indian roads. In this study, we present a comprehensive framework for the detection of violations, the notification of offenders, and the storage of violations for statistical analysis and decision-making related traffic regulations policy. In the suggested method, we first identify automobiles using object detection, which is carried out using YOLO, and then each vehicle is afterwards examined for the relevant infractions, such as not wearing a helmet or violating crosswalks. A classifier based on CNNs (convolutional neural networks) is used to identify helmet violations. Using the Instance Segmentation by Mask R-CNN architecture, crosswalk violations are found. Following the discovery of violations, the relevant violators' car numbers are retrieved via OCR, and the violators are contacted. Thus, a fully autonomous system will aid in the enforcement of strict traffic regulations.
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1 complete working code implementation in clients machines
2 complete classes on project will be held
3 documentation support
4 Help in researching without extra cost/price increase.