关键词: 关键词: 道路病害检测, DEIM, 多尺度特征, 细粒度, 复杂背景

Abstract: Abstract: Precise detection of road pavement distress was significant for ensuring traffic safety, extending road service life, and enhancing maintenance decision-making efficiency. To address poor robustness under complex background interference and low recognition accuracy for multi-scale distresses such as minute cracks in existing algorithms, an enhanced road distress detection algorithm based on an improved DEIM framework was proposed. First, a Cross-scale Feature Interaction Enhancement Structure (CCFS) was designed. By optimizing the cross-scale feature interaction mechanism to compensate for fine-grained texture information, the structure enhanced the model's capability to capture minute distress targets. Second, a texture-aware Enhanced Encoder (EAIFI) was designed to strengthen local discriminative capacity and noise robustness in complex environments. Finally, an Efficient Channel Attention (ECA) module was embedded at the front end of the decoder to suppress unstructured environmental interference and directionally enhance key distress responses. Experimental results on the public SVRDD dataset demonstrated that, compared with the baseline DEIM-D-FINE-S, the improved model achieved increases of 2.3, 3.8, and 3.1 percentage points in AP@0.50, AP@0.75, and AP@0.50:0.95, respectively, with only an increase of 0.3?M parameters and 1.8?GFLOPs in computational cost. Compared with the suboptimal DP-DETR algorithm, the improved model also achieved a higher AP@0.50:0.95 by 1.6 percentage points. The algorithm achieved a favorable balance between detection accuracy and computational cost, thus providing a feasible technical solution for automated road distress detection and maintenance.

Key words: Keywords: Road distress detection, DEIM, Multi-scale feature, Fine-grained, Complex background