Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle

在一座老化混凝土桥上测试一套流程，从商用无人机拍摄的照片中检测裂缝并测量其宽度和长度。裂缝由在小规模训练集上微调的 R-CNN 检测，贴在结构上的 70 mm 方形标记提供像素比例尺，并用单应变换校正畸变。由于桥下 GPS 不可靠，飞行以约 2 m 距离手动进行。现场共测量了 12 条裂缝，但未在现场进行真值核对。

主要发现

• Lab test relative error about 1-2% for crack quantification
• Field: 12 cracks measured at about 0.53-2.47 mm thick and 6.6-78.4 mm long
• Processing about 150 min for the point cloud and about 14 min for deep-learning detection

局限

• Field measurements not checked against ground truth; no precision/recall reported
• Requires a physical marker on the structure for scale
• Manual flight; GPS unreliable under the bridge
• One bridge, only the rails inspected; small training set (384 images)