From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. The extraction of pavement cracks is always a hard task in image processing.
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