Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

将 Faster R-CNN 目标检测改造为可同时识别多种损伤类型，而不仅限于裂缝：混凝土裂缝、中度和重度钢材锈蚀、螺栓锈蚀以及钢材分层。在带标注的图像集上训练后，其单张图像的处理速度足以支撑基于视频的损伤检测框架。摘要中并未将其定位为无人机研究。

主要发现

• Mean average precision 87.8% across five damage classes (class APs 90.6, 83.4, 82.1, 98.1, 84.7%)
• 0.03 s per 500x375 image, enabling a near real-time video framework
• Dataset of 2,366 labelled images

局限

• Abstract only reviewed
• Not described as UAV imagery; robustness test used only 11 new images
• Bounding boxes, not damage size or severity measurement