# RMR-P: Road Metadata-Aware Restoration for Pavement Inspection

arXiv: https://arxiv.org/abs/2608.08957 · HTML: https://mlsanity.com/paper/2608.08957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Degradation-Aware Feature Perturbation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.12630/) (2025)
- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Gyroscope-Aided Motion Deblurring with Deep Networks](https://mlsanity.com/paper/1810.00986/) (2018)
- [TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning](https://mlsanity.com/paper/2209.01373/) (2022)

## Abstract (arXiv)

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.