Improved YOLOv3 Object Classification in Intelligent Transportation System
This work addresses driver detection for traffic supervision and public safety in Intelligent Transportation Systems, but it is incremental as it builds on existing YOLOv3 methods.
The paper tackled the problem of detecting and classifying vehicles, drivers, and people on highways to distinguish drivers from passengers and match vehicles to drivers, using a YOLOv3-based algorithm; it achieved good performance and robustness under complex conditions, as validated on a self-built database.
The technology of vehicle and driver detection in Intelligent Transportation System(ITS) is a hot topic in recent years. In particular, the driver detection is still a challenging problem which is conductive to supervising traffic order and maintaining public safety. In this paper, an algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway, so as to achieve the purpose of distinguishing driver and passenger and form a one-to-one correspondence between vehicles and drivers. The proposed model and contrast experiment are conducted on our self-build traffic driver's face database. The effectiveness of our proposed algorithm is validated by extensive experiments and verified under various complex highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.