RGD-DETR: Road Garbage Detection Based on Improved RT-DETR

Rapid urbanization in China has led to an increase in the volume of daily road garbage, posing challenges to municipal sanitation. Automatic garbage collection is thus essential for sustainable management. This paper proposes an improved RT-DETR-based (Real-Time Detection Transformer) detection model, RGD-DETR, to improve road garbage detection performance. Firstly, an improved feature pyramid module that leverages multi-scale feature fusion techniques to enhance feature extraction effectiveness is designed. Secondly, a state space model is introduced to accurately capture long-range dependencies between image pixels with its spatial modeling capability, thus obtaining high-quality feature representation. Thirdly, a Dynamic Sorting-aware Decoder is adopted to embed a dynamic scoring module and a query-sorting module in adjacent decoder layers, enabling the model to focus on high-confidence predictions. Finally, the classification- and localization-oriented loss and matching cost are introduced to improve target localization accuracy. The experimental results on the road garbage dataset show that the RGD-DETR model improves detection accuracy (mAP) by 1.8% compared with the original RT-DETR, performing well for small targets and in occlusion scenarios.