A lightweight Tiny-YOLOv3 vehicle detection approach

In recent years, vehicle detection from video sequences has been one of the important tasks in intelligent transportation systems and is used for detection and tracking of the vehicles, capturing their violations, and controlling the traffic. This paper focuses on a lightweight real-time vehicle detection model developed to run on common computing devices. This method can be developed on low power systems (e.g. devices without GPUs or low power GPU modules), relying on the proposed real-time lightweight algorithm. The system employs an end-to-end approach for identifying, locating, and classifying vehicles in the images. The pre-trained Tiny-YOLOv3 network is adopted as the main reference model and subsequently pruned and simplified by training on the BIT-vehicle dataset, and excluding some of the unnecessary layers. The results indicated advantages of the proposed method in terms of accuracy and speed. Also, the network is capable to detect and classify six different types of vehicles with MAP = 95.05%, at the speed of 17 fps. Hence, it is about two times faster than the original Tiny-YOLOv3 network.