In response to the difficulty of on-site inspection of concrete walls in pumped storage power plants and the low efficiency of traditional inspection methods,A crack detection system based on deep learning is proposed.By matching the similarity between FCN network and CNN network,the location,size,and depth of concrete cracks can be effectively determined,and automatic detection of cracks can be achieved.At the same time,the hardware architecture and software system of the inspection robot were designed to achieve functions such as autonomous planning of inspection routes,autonomous navigation,and autonomous identification of detection areas.The performance of the proposed detection model was verified and tested based on the CrackForest dataset,and the results showed that the recognition accuracy of the proposed model can reach 87.74%,with an average error of only 0.45,indicating good overall performance.
关键词
抽水蓄能电站/裂缝检测/图像识别/深度学习/智能巡检
Key words
pumped storage power station/crack detection/image recognition/deep learning/intelligent inspection
维普
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