Research on Pavement Anomaly Detection Technology Based on Improved RT-DETR

Pavement anomaly detection holds significant practical importance for ensuring driving safety, optimizing traffic management, and enhancing driving experience. To address the challenges posed by variations in the size, shape, and color of pavement anomalies, and complex environmental interferences that lower detection accuracy and efficiency, this study proposes an improved Real-Time Detection Transformer (RT-DETR)-based technology for pavement anomaly target detection. First, a Large Receptive Field Element-wise Multiplication Block (LRFEM_Block) is designed to replace the BasicBlock module in the original backbone network, effectively enhancing feature expression capabilities based on the element-wise multiplication principle. Next, a Generalized Efficient Layer Aggregation Network (GELAN) is introduced and combined with multi-scale LRFEM_Block modules to design a Multiplicative-based Layer Aggregation Intra-scale Feature Interaction (MLA-IFI) structure, which improves the computational efficiency and performance of the neck network for deep features and optimizes the gradient propagation path. Additionally, the Selective Boundary Aggregation (SBA) concept is employed to construct a Bidirectional Adaptive Boundary Fusion Feature Pyramid Network (BABF-FPN) multi-scale feature fusion module, adaptively aggregating features of different resolutions bidirectionally and promoting the refinement of small object boundaries. Experimental results show that the improved method achieves a 3.4 and 4.7 percentage point increase in mAP@0.5 on a self-built dataset and the RDD2022 public dataset, respectively, outperforming other models. Moreover, it reduces the number of parameters and computational load by 24.5% and 11.2%, respectively, with a detection speed of 74 frame/s, thereby satisfying the deployment requirements for in-vehicle pavement anomaly detection.