Concrete Surface Crack Recognition in Complex Scenario Based on Deep Learning

A concrete crack detection method suitable for various scene conditions is proposed on the basis of image recognition to complete the classification and the identification of the cracks of the collected crack images in the bridge health monitoring work conveniently and reliably. Moreover, this method can improve the crack recognition effect, which is greatly affected by the selection of the initial clustering center of the extraction algorithm, and has high environmental dependence on the image background. Convolutional neural networks (CNNs) is a representative deep learning algorithm that can characterize learning and classify input information according to its own hierarchical structure. After collecting image data on the spot by relying on the Baoxie River Bridge inspection project, Gaoxin 4th Road, Donghu High-tech Zone, Wuhan City based on the CNN, an image classification model suitable for concrete crack image classification is established. This model realizes the collection of the inspection project image classification of concrete structures in complex scenes while considering that the local cluster density and Euclidean distance of the clustering center in the traditional K-means algorithm are both large. The traditional K-means algorithm is improved by combining the use of statistical principles and morphological methods. Finally, the improved K-means algorithm completes the crack skeleton segmentation extraction and crack width calculation of crack images in complex scenes. The effectiveness of the proposed method under concrete surface peelings, stains, mosses, or other noise conditions was verified according to the successful identification of 600 crack on-site images photographed from a bridge surface. Results also show that the efficiency of the proposed crack detection method is higher than that of traditional methods. The proposed approach can also provide a reference for the in depth research on crack identification on the surface of concrete structures in complex background in the future.