A smart bus lane monitoring system based on object detection

In this paper, we introduced a lightweight object detecting system which can help to detect illegal occupation in bus lane. This system can play a role to make the city smarter. The system can classify vehicles into social cars and buses, recognize and detect car licenses. At the beginning, we introduced relevant works done by other researchers which turned out to be inspiring. Next, we designed the structure of our system into three different parts: central service module, server, and embedded device. Each part plays different roles. The central service module realizes the function of the front-end page and back-end of the platform by applying micro-service architecture. Server is responsible for works related to model training and communication between embedded device and cloud server. Embedded device should be implemented on buses, detecting, and recording illegal occupation of bus lane by running the trained object detection model. The system can realize different functions by implementing different models. We used deep learning to realize our expected function. First, a dataset includes two kinds of vehicle, car plate, 10 Arabic numbers, 26 English letters, and 6 Chinese abbreviation of 6 Chinese provincial districts. Then we use YoloV5 to train the model. After the model is trained, we evaluated the model. The results indicate that the model matches our expectation.