Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

Abstract

Concrete is extensively used in the construction of infrastructure such as houses and bridges. However, the appearance of cracks in concrete structures over time can diminish their sealing and load-bearing capability, potentially leading to structural failures and disasters. The timely detection of cracks allows for repairs without the need to replace the entire structure, resulting in cost savings. Currently, manual inspection remains the predominant method for identifying concrete cracks. However, in today’s increasingly complex construction environments, subjective errors may arise due to human vision and perception. The purpose of this work is to investigate and design an autonomous convolutional neural network-based concrete detection system that can identify cracks automatically and use that information to calculate the crack proportion. The experiment’s findings show that the trained model can classify concrete cracks with an accuracy of 99.9%. Moreover, the clustering technique applied to crack images enables the clear identification of the percentage of cracks, which facilitates the development of concrete damage level detection over time.

Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

“…Zheng et al [18] integrated a tensor voting module into semantic segmentation network, enhancing the feature map by incorporating significant domain maps generated through tensor voting. Choi et al [19] utilized the ResNet50 network model to extract features from concrete crack images, subsequently applying the Sobel edge detection operator to amplify crack characteristics and diminish background noise, and employed the K-Means clustering algorithm to ascertain crack distribution, offering an automated and effective approach for assessing and monitoring damage in concrete structures. Luo et al [20] combined the Canny result images with the low-level features within DeeplabV3+ network, enriching pavement crack location details and compensating for detail loss during the fusion of high-level and low-level feature layers.…”