基于改进RT-DETR模型的道路减速带检测算法OA

In complex urban road environments,uneven road structures such as speed bumps pose potential safety threats to autonomous vehicles.Inaccurate or delayed detection not only affects ride comfort but may also cause vehicle damage or safety incidents.Therefore,the robustness of perception systems under varying illumination,occlusion,and texture conditions is crucial for decision-making and control accuracy in autonomous driving.To this end,we propose an improved algorithm,SDFP-DETR,based on the RT-DETR model to enhance the performance and efficiency of speed bump detection.Specifically,SPCA attention module is integrated into the backbone to strengthen feature representation in key regions and improve detection stability in complex scenes.In the neck network,an AIFI-DHSA module based on a dual-head self-attention mechanism is designed to enhance multi-scale semantic feature modeling.Furthermore,a Focaler-PIoU loss function is adopted to improve gradient response to low-quality samples and accelerate model convergence.Experimental results on a self-built complex road scenario dataset show that the proposed model achieves 5.2 percentage points increase in precision,2.0 percentage points increase in recall,and3.3 percentage points improvement in mAP50.Compared with existing methods,the proposed approach maintains high detection performance under challenging illumination and occlusion conditions,demonstrating excellent environmental adaptability and application potential.