| dc.description.abstract | Improving traffic chaos and creating a safe driving environment has always been t he goal of all countries. In the past, without the help of any technological methods, traffic violations were reported by police officers on the spot or by providing the video to the poli ce for prosecution, whether on the spot or afterwards, a lot of human resources were neede d. Because the thrive of artificial intelligence in recent years, technology law enforcement has also been born. It is hoped that by using the help of AI to report traffic violations dire ctly, it can significantly reduce manpower expenses, and can also achieve 24-hour monitori ng.
The most common technology enforcement is to use the object detection model to d etect the vehicle first, and then write the violation algorithm based on different fields and different violations. This is the fastest way to implement technology enforcement, but the disadvantage is that we need to decide the violation rules to be monitored according to dif ferent scenes, and write the violation algorithm for the violation rules. In order to solve th e problem of identifying the different violations in different scenes, we hope to use deep lea rning models to learn different traffic violations by using images of different violations.
One stage object detection model YOLOv4 can already achieve the advantage of go od detection effect and fast speed. Therefore, this paper also adopts a YOLO-like architect ure to directly identify vehicle parking violations. We enhance the feature extraction capa bility and feature fusion capability of the model through RFSM, STEM, and FFAM modul es, so as to improve the capability of model identification.
We will also use Grad-CAM to visualize the attention of the model. What we hope is that the model can learn the traffic rules made by humans, such as learning how human s judge whether the violation is valid or not. | en_US |