Improving YOLO11 Architecture for Reckless Driving Detection on the Road

Reckless driving behavior on the road can increase the risk of traffic accidents for drivers and other road users. Currently, supervision remains weak, particularly in direct supervision, due to the limited number of officers. This study developed an automated system to detect reckless drivers based on their road trajectories. This system comprised three subsystems: car detection, car tracking, and driving trajectory detection. In the driving trajectory detection subsystem, we proposed an improved YOLO11n-cls method developed from YOLO11n-cls by adding convolution and C3k2 blocks. The test results showed that the proposed model achieved an accuracy increase of 4.4% over YOLO11n-cls. The proposed model achieved an accuracy of 0.935 and an inference time of 0.5 ms for car trajectory classification. In addition, the proposed model achieved higher accuracy than all YOLO11 models (YOLO11n-cls, YOLO11s-cls, YOLO11m-cls, YOLO11l-cls, and YOLO11x-cls) and all YOLO12 models (YOLO12n-cls, YOLO12s-cls, YOLO12m-cls, YOLO12l-cls, and YOLO12x-cls). Therefore, the proposed model is better suited to support traffic law enforcement, especially the real-time detection of reckless drivers on highways.

@article{Sutikno2025,
  title     = {Improving YOLO11 Architecture for Reckless Driving Detection on the Road},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sutikno and Aris Sugiharto and Retno Kusumaningrum},
  doi       = {10.14569/IJACSA.2025.0161258},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161258}
}