ABSTRACT To address the issues of missed detections, false positives and feature degradation in road debris detection caused by small and irregular targets, this study proposes a novel framework integrating multi‐scale feature fusion and dynamic feature enhancement mechanisms. It also constructs a dedicated road debris dataset to fill the gap in public benchmark datasets in this field. Firstly, a cross‐layer connection‐optimized feature fusion network is designed in the neck network, addressing the limitation of insufficient fusion of shallow and deep features in existing methods, realizing efficient linkage between shallow texture features and deep semantic information, and significantly improving the detection capability for small targets. Secondly, a context‐aware anchor attention module integrating reparameterized convolution and adaptive weight allocation is embedded into the backbone network. Compared with traditional fixed receptive field convolution, it can dynamically enhance target features and suppress background interference, effectively solving the problem of feature degradation in complex environments. Thirdly, an improved spatial pyramid fast pooling module based on global pooling and Ghost convolution is proposed, overcoming the defect of prone detail loss in traditional max‐pooling and preserving key information of small‐sized road debris to the greatest extent. Finally, a weighted fusion loss function integrating corner distance loss, focal loss, cross‐scale correlation loss and CIoU loss is designed, breaking the limitation of insufficient attention to irregular targets in a single loss function and enhancing the model's adaptability to complex scenes. Experimental results show that the framework outperforms existing mainstream methods in road debris detection scenarios, achieving a precision of 91.5%, a recall of 82.0% and an mAP50 of 88.7%.
Guo et al. (Thu,) studied this question.