Helmet detection in traffic scenarios: enhanced performance for complex environments (opens in new tab)

In the cutting-edge development of computer vision technology, the high missed detection rate caused by target occlusion and the high computational cost of model inference remain core technical bottlenecks restricting the deployment of robust object detection systems in real-world scenarios. This paper presents an improved YOLOv10n-based algorithm to address these issues. Adaptive-DySample (ADS) enhances target detection via three innovative mechanisms—dynamic alignment, challenging sample pr...