Concrete detection method based on image processing

In order to save the time of artificial crack identification and improve the efficiency of concrete detection, the convolution neural network image detection model is established by MATLAB. The influence of image reconstruction sampling rate, binary threshold and three detection operators (Laplace operator, Sobel operator and Canny operator) on concrete crack detection are studied. By adjusting different parameters, it can be concluded that the optimal sampling rate is 0.3 and the optimal threshold is 0.4 for the concrete crack image. Canny operator has the best effect on edge detection and crack detection, and can comprehensively reflect the edge crack situation, followed by Sobel operator, the effect of Laplace operator is poor. The convolution neural network image detection model can provide technical methods for the detection of concrete structures in complex environment in the future.