Advanced Traffic Safety System Utilizing AI for Speeding, Red Light, and Rider Monitoring
Keywords:
YoloV7-X, Pytesseract, computer vision, object-detection, bounding box, Precision, Tensor board, Recall, F-measure, P-measure, Fully Connected Neural Network (FCNN)Abstract
According to recent reports, traffic violations have mostly resulted in an increase in fatalities and injuries on Indian
roads. Because manually identifying traffic violations takes time, an automatic computer vision-based object
identification model was required. The fundamental idea behind this research is to identify many transgressions
using a single video frame. To perform various activities, the security camera's input video stream is processed
and annotated. COCO is the dataset utilized for red-light leaping, while Google pictures are annotated to provide
the dataset for over boarding. Tensor board is used to train the model and visualize its results. The criteria
employed include precision, recall, F-measure, and P-measure. Red light skipping accuracy is 93%, and the over
boarding mAP value is 0.5:0.95. This system makes extensive use of the video feed to detect various forms of
breaches.
