用图像修复提升无人机低光桥梁损伤检测，让模糊缺陷变清晰。
Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement
- 设计自适应修复模块，分三路处理噪声、色彩和细节恢复。
- 合成数据上使检测框准确率从31%提升至49%，掩膜准确率从23%升至35%。
- 无需正常光照对照图，适合真实桥梁巡检场景，尤其低光环境。
光照不足导致无人机拍摄的桥梁图像中微小、低对比度缺陷难以辨识，降低自动化检测的可靠性与灵活性。本文探究退化感知图像恢复能否在低光条件下提升桥梁损伤检测性能，并实现从合成退化到真实场景的迁移。提出DaL-MoE：一种与检测器无关的恢复前端，基于ISP感知的低光合成流程训练，包含退化感知引导估计与互补专家，分别用于噪声抑制、色彩校正和结构细节恢复。在配对合成数据上，达23.12 dB PSNR和0.8482 SSIM，YOLOv11m的box mAP50从0.3097提升至0.4923，mask mAP50从0.2281增至0.3529。在无配对正常光参考的真实低光无人机图像上，模拟跨域评估显示修复后缺陷可见性更好，检测更完整，优于直接对原始低光图像推理。未来工作将开发具备更强跨桥址、跨成像条件与光照水平泛化能力的低光感知损伤检测器。
原文摘要 · Abstract (English)
Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspection. This paper investigates whether degradation-aware image restoration can improve bridge damage detection under low-light conditions and transfer from synthetic degradations to real inspection scenes. We propose DaL- MoE, a detector-agnostic restoration front end trained with an ISP-aware low-light synthesis pipeline and equipped with degradation-aware guidance estimation and complementary experts for noise suppression, color adjustment, and structural-detail recovery. On paired synthetic data, DaL-MoE achieves 23.12 dB PSNR and 0.8482 SSIM, increasing YOLOv11m box mAP50 from 0.3097 to 0.4923 and mask mAP50 from 0.2281 to 0.3529. On real low-light UAV imagery without paired normal-light references, sim-to-real evaluation shows improved defect visibility and more complete detections than direct inference on raw low-light inputs. Future work will develop low-light-aware bridge damage detectors with stronger cross-scene generalization across bridge sites, imaging conditions, and illumination levels.
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