A Two-Step Real-Time Complex Environmental Vehicle Detection Model

In recent years, as a critical pillar supporting the national economy and daily life, the safe and efficient operation of road traffic has highly relied on precise environmental perception capabilities. To address this, this study proposes a two-stage “denoising-detection” framework: the first stage restores clear images using an improved Uformer algorithm, which funda-mentally merges a probabilistic sparse self-attention mechanism. In contrast, the second stage leverages YOLOv11 for real-time object detection. This framework is introduced in the field for the first time and enhances the accuracy and robustness of vehicle detection in traffic images under complex weather scenarios, providing technical support for intelligent driving systems and traffic monitoring applications. Our experimental validation on our own flexible weather car detection database demonstrated the superior performance of the proposed model: CM-YOLO achieved 0.95 precision and 0.91 mAP50, which promoted 0.2 than the YOLOv11.

@article{Huo2025,
  title     = {A Two-Step Real-Time Complex Environmental Vehicle Detection Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Zhihui Huo and Yiqian Liang and Xingju Wang},
  doi       = {10.14569/IJACSA.2025.01612116},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612116}
}