为解决大型特种设备不可达部位表面缺陷检测的难题，提出了一种利用无人机检测和识别表面裂纹的方法。首先利用搭载着双云台的无人机检测装置，对罐区围堰墙面以及高空建筑物墙面等目标的表面图像进行全方位采集；然后用Faster R-CNN深度学习神经网络算法对采集到的图像进行分类，确定检测图像中是否存在裂纹缺陷；最后对检测出的裂纹目标框区域进行形态学处理。检测结果表明，Faster R-CNN算法的裂纹检测准确率达95.74%，同时裂纹宽度识别误差约为3.9%，长度误差约为5.3%，实现了罐区围堰墙面以及高空建筑物墙面的远程自动化检测。

To solve the problem of surface defect detection in inaccessible parts of large-scale special equipment, a method using unmanned aerial vehicle (UAV) to detect and identify surface cracks was proposed. Firstly, a UAV detection device equipped with a dual pan-tilt-zoom (PTZ) platform was used to comprehensively collect surface images of the tank farm cofferdam walls and high-altitude building walls; Then, the Faster R-CNN deep learning neural network algorithm was used to classify the collected images and detect whether there were cracks or defects in the images; Finally, morphological processing on the detected crack target box area was performed. The detection results showed that the Faster R-CNN algorithm had a crack detection accuracy of 95.74%, with a crack width recognition error of about 3.9% and a length error of about 5.3%. It had achieved remote automated detection of the tank farm cofferdam wall and high-altitude building wall.