Detection and recognition of concrete cracks on building surface based on machine vision

Surface cracks need to be detected regularly to ensure the safety of concrete buildings. For the sake of efficiency and accuracy, concrete surface cracks are detected by machine vision technology. This paper briefly introduced the convolutional neural network (CNN) algorithm used for identifying concrete surface cracks. Then, the traditional CNN algorithm was improved by the particle swarm optimization (PSO) algorithm, and it was compared with the support vector machine (SVM) algorithm and the traditional CNN algorithm in the simulation experiment. The results showed that the improved CNN algorithm effectively identified the concrete surface cracks with different cracking degrees; moreover, the precision ratio, recall ratio, and F value of the improved CNN algorithm were superior to those of SVM and traditional CNN algorithms in recognizing cracks on the concrete surface, and the training and testing time was shorter than that of SVM and CNN algorithms.