Automated crack segmentation in close-range building façade inspection images using deep learning techniques
Nowadays, unmanned aerial vehicles (UAVs) are frequently used for periodic visual inspection of building envelopes to detect unsafe conditions or vulnerable damages. Inspection practitioners have to manually examine the large amounts of high-resolution images collected by UAVs to identify anomalies or damages on building facades for reporting and repairs. The computer vision and deep learning technologies have emerged as promising solutions to automate the image-based inspection process.