Automatic Vehicle Plate Recognition and Overspeed Detection

Road accidents are a major issue, and their occurrence has consistently risen. Therefore, implementing a system is essential in reducing these accidents. Based on statistics from highway accidents, the main reason for accidents is vehicles driving too fast. Despite the presence of speed limit signboards on all highways, many drivers opt to disregard these regulations. At present, police officers use handheld radar guns to monitor vehicle speeds and report the data to authorities manually. The goal is to develop a machine learning system capable of automatically detecting over speeding and identifying vehicle plates. This paper aims at improving road safety by precisely identifying vehicles that are speeding and by instantly recognizing license plates. The suggested system in this paper removes the necessity for human involvement and autonomously logs vehicle speeds while notifying authorities of any infractions. This paper creates a system using the machine learning model CNN that can identify speeding and record the license plate of the vehicle using a camera. The technology of image processing is utilized to capture the image and subsequently transmit it to the RTO via a server. The research paper seeks to enhance road safety by precisely detecting vehicles that surpass speed limits. During testing, the system achieved an accuracy of 80.2% in identifying overspeed and 83% in detecting license plates.