Smart Traffic Surveillance: Identifying Helmet-Less Riders and License Plate Recognition with YOLO and OCR Integration using Deep Learning
1G. Shaheena, 2D. Susmitha, 3R.B. Swathi, 4B. Vinaya Pratheeka, 5Mrs. I. Sravani
1,2,3,4UG Students, Department of Computer Science and Engineering, Sai Rajeswari Institute of Technology, Proddatur, Andhra Pradesh-516360
5Assistant Professor, Department of Computer Science and Engineering, Sai Rajeswari Institute of Technology, Proddatur, Andhra Pradesh-516360
Email: godugushaheena@gmail.com, dumsasusmitha14@gmail.com, rbswathi36@gmail.com, vinayapratheeka333@gmail.com, sravani.santoshi@gmail.com
Abstract: Currently, there are a number of issues with Indian traffic laws that can be resolved in a variety of ways. Riding a motorbike without a helmet is against the law and has increased the number of accidents in India. The current method mostly uses CCTV records to monitor traffic offences. When a rider is not wearing a helmet, traffic officers must zoom in on the licence plate and look into the frame where the infraction is occurring. However, because there are more and more traffic infractions and motorcycle ridership every day, this will take a lot of labour and time. What if there was a technology that would automatically search for traffic violations such as riding a motorbike without a helmet? hence, would automatically retrieve the number plate number of the bike. This study has been effectively completed by recent research using KNN, DNN-Classifier, etc. However, there are limitations to these works in terms of speed, accuracy, or efficiency in object identification and classification.
The goal of this research project is to develop a non- helmet rider detection system that can automatically identify and retrieve a vehicle's number plate number when a driver fails to wear a helmet. Three-level Deep Learning for Object Detection is the key idea at play here. The items found are a human, a motorcycle at level one using YOLO, a helmet at level two using YOLO, Licence plate at the last level using YOLO. Next, OCR is used to obtain the number plate registration number.
All of these methods—especially the section where the number plate number is extracted—are subject to predetermined limitations and conditions. Because video is the work's input, speed of execution is essential. We have developed a comprehensive algorithm for both number plate number extraction and helmet detection using the above-mentioned approaches.
