修复道路图像模糊噪声，提升裂缝坑洼检测精度。
RMR-P: Road Metadata-Aware Restoration for Pavement Inspection
- 根据图像自估退化特征，结合外部参数指导修复。
- 在八种退化条件下，七种达最优检测精度，最高提升至0.427 mAP50。
- 保留路面细粒度纹理是检测提升的关键，适合道路巡检场景。
车载摄像头拍摄的道路图像常因车辆运动、相机限制和环境变化导致运动模糊、失焦、光照不良和噪声等问题，掩盖细小裂缝与坑洼边界，影响缺陷检测准确性。本文提出RMR-P修复网络，通过分析输入图像自估退化特征，并可选地引入外部退化参数引导修复。为评估恢复信息对下游检测的增益，使用未微调的YOLO11s检测器在退化与修复图像上进行测试。在IVCNZ与PCM数据集上的实验表明，RMR-P在八个独立退化条件中，有七个达到最高mAP50；其中，IVCNZ运动模糊下从0.140提升至0.427，PCM失焦下从0.060提升至0.233。消融实验证明，保留细粒度路面细节（细节保持路径）对检测提升贡献最大，退化条件引导与任务导向训练提供互补增益。
原文摘要 · Abstract (English)
Road-surface images captured by vehicle-mounted cameras are often degraded by motion blur, defocus, poor illumination, and noise due to vehicle motion, camera limitations, and varying environmental conditions. These degradations can obscure thin cracks and pothole boundaries that are critical for accurate road-defect detection. This paper presents RMR-P, a restoration network designed to recover defect-relevant information from degraded road images. It estimates degradation characteristics from the input image and can optionally incorporate external degradation parameters to guide restoration. To evaluate whether the recovered information improves downstream detection, a clean-trained YOLO11s detector is applied to degraded and restored images without further modification. Experiments on the IVCNZ and PCM datasets, with known synthetic degradation parameters provided as conditioning information, demonstrate that RMR-P achieves the highest mAP50 in seven of eight held-out degradation conditions, including improvements from 0.140 to 0.427 under IVCNZ motion blur and from 0.060 to 0.233 under PCM defocus. Moreover, our ablation studies show that preserving fine pavement details (detail-preserving pathway) provides the largest contribution to defect-detection improvement, while degradation conditioning and task-guided training offer complementary benefits.
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