To address the challenge of obtaining a large volume of precisely annotated sample data for automatic crack detection tasks,LGS-Net(Local Global Similarity Network)is proposed in the paper.The core of LGS-Net is to utilize the semantic similarity of the crack image region,effectively combine a small amount of labeled data and a large amount of unlabeled image data,and realize the automatic crack detection by semi-supervised learning through to realize automatic crack detection.To comprehensively evaluate the performance of LGS-Net,the experiments are validated on GAPs384 and Crack500 datasets.The results show that LGS-Net is able to achieve high-precision crack detection with limited labeling resources.The ability of LGS-Net to effectively identify cracks in complex environments is demonstrated through the visual analysis of the detection results.LGS-Net utilizes the semantic similarity features of pavement crack images for detection,which provides technical support for engineering applications of pavement crack detection.
维普
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