Road Distress Detection Method Based on Improved YOLO11 for Unmanned System Inspection

Abstract

With the integration of unmanned systems, aerospace remote sensing, and artificial intelligence, intelligent inspection for road infrastructure has become an important support for geohazard early warning, disaster-prevention spatial planning, and resilient urban governance. To address the slender and curved morphology of cracks, obvious scale variation, complex background interference, and missed detection of small targets in road distress detection, this paper proposes an improved YOLO11-based road distress detection method. The method designs a C3k2_DSConv module to replace the original C3k2 module of YOLO11, thereby enhancing local morphological modeling for irregular crack-like targets. A VimLayer and an AIFI module are designed and introduced to strengthen long-range continuous-structure representation and global semantic modeling. In addition, EMA attention modules are inserted before multi-scale detection features to highlight key distress regions. Experiments are conducted on the merged China and Japan subsets of RDD2022, which contain 11,753 images and 24,188 annotated bounding boxes. Experimental results show that, compared with YOLO11, the improved YOLO11 model increases mAP50 from 0.641 to 0.672 and Recall from 0.568 to 0.615. The proposed method can support UAV- and UGV-based road inspection and provide a visual perception basis for infrastructure condition assessment and resilient urban governance.