Automatic crack detection and calculation based on deep learning and digital image processing

Structural health monitoring is an important way to ensure the safe and reliable work of the structures during the service period. Surface crack is one of the important indicators for monitoring and assessing the damage of structures. However, crack detection still mainly relies on human visual observation and recording. Besides, people could only record the crack width of a few points instead of all points. To solve this problem, this paper aims to propose a method which can automatically detect crack and calculate the width of every point on the crack but not several points. Thus, this paper built a large database containing more than 100,000 pictures which were taken in different scenarios, and used this database and self-designed deep convolutional neural network to detect cracks. And the prediction accuracy of CNN reached 95%. In addition, combined with image processing technology, this paper further carried out crack extraction and width calculation. Thus, the whole process of crack detection, extraction and width calculation is fully automated, and the calculation results were proved to be close to the actual results, which meets the engineering requirements and perform better than traditional OTSU algorithm.