Grand challenge of image processing in automatic detection of vehicles running in red lights

With more and more vehicles in the city, traffic violations are becoming more and more serious. Vehicle violations rely on manual interpretation, which is not only inefficient, but also leads to a backlog of data and inconsistent law enforcement standards. In response to this situation, this paper proposes an artificial-intelligence-based algorithm for automatic interpretation of illegal behaviors of vehicles running red lights. This paper first discusses the modeling process of building a vehicle running a red light, including image data preprocessing, traffic light and vehicle recognition, vehicle red light detection, and license plate recognition; then an automatic interpretation algorithm for vehicle red light violations is designed. Finally, the actual traffic photos are used to test the algorithm. The experimental results show that the algorithm has a high recognition rate and can effectively automatically identify the illegal behavior of vehicles running red lights, so it can effectively solve the problem of low efficiency of manual judgment.