RMR-Net：面向缺陷检测的退化证据引导道路图像恢复
RMR-P: Road Metadata-Aware Restoration for Pavement Inspection
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中文总结 AI 辅助
针对车载道路图像退化导致缺陷检测失效的问题，提出RMR-Net恢复前端，在IVCNZ和PCM数据集上的8种退化条件中7种获最高mAP50，有界细节路径是核心贡献项。
中文摘要 AI 辅助
车载道路摄像头易受运动模糊、散焦、光照不足和噪声影响，这会抹去道路缺陷检测器所需的细裂缝和坑槽边界。本文提出RMR-Net，一种紧凑的任务感知恢复前端，它从图像中估计退化证据，可选择将其与现有损坏上下文/参数融合，对轻量型恢复模块进行条件约束，并通过有界残差路径返回高频路面细节。实验范围被刻意控制：在新西兰图像与视觉计算（IVCNZ）坑槽数据集和道路损伤数据集（坑槽、裂缝和检查井，PCM）上使用的条件信息是保存的合成生成器参数，而非实测车辆遥测数据。用经干净图像训练并冻结的YOLO11s检测器评估每个图像源。在8种预留退化条件中，RMR-Net在7种条件下获得最高mAP50，其中IVCNZ运动模糊下为0.140-0.427，PCM散焦下为0.060-0.233。紧凑消融实验表明，有界细节路径是最大局部贡献项，而退化条件和感知器稳定性项提供互补指导。
英文摘要
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.
