Concrete crack detection and 3D mapping by integrated convolutional neural networks architecture

Abstract

This paper presents an image-based crack detection system, in which its architecture is modified to use deep convolutional neural networks in a feature extraction step and other classifiers in the classification step. In the classification step, classifiers including Support Vector machines (SVMs), Random Forest (RF) and Evolutionary Artificial Neural Network (EANN) are used as an alternative to a Softmax classifier and the performance of these classifiers are studied. The data set was created from various types of concrete structures using a standard digital camera and an unmanned aerial vehicle (UAV). The collected images are used in the crack detection system and in creating a 3D model of a sample concrete building using an image- based 3D photogrammetry technique. Then, the 3D model is used to create a mosaic image, in which the crack detection system was applied to create a global view of a crack density map. The map is then projected onto the 3D model to allow cracks to be located in the 3D world. A comparative study was conducted on the proposed crack detection system and the results prove that the combined architecture of CNN as a feature extractor and SVM as a classifier shows the best performance with the accuracy of 92.80. The results also show that the modified architecture by integrating CNN and other types of classifiers can improve a system performance, which is better than using the Softmax classifier.

Research on Computer Classification Algorithm of Concrete Crack Based on Deep Learning

“…Since the shape, direction, size and distribution of each crack are not the same, coupled with the influence of external conditions such as external lighting, how to accurately obtain the deep internal characteristics of the crack is an important link to realize the fine division of the crack. However, the encoder of the left wing of the original U-net model has great limitations, resulting in the effective characterization of the crack [12]. And a large number of negative samples are easy to identify, resulting in the failure of U-net model.…”

CPAM: Cross Patch Attention Module for Complex Texture Tile Block Defect Detection

“…However, there are a few scholars who have explored the potential of deep learning models in the field of tile block defect detection. Chaiyasarn et al [12] use a simple CNN architecture for tile surface defect detection in temples, which has greater stability and higher accuracy compared to artificial neural networks (ANN). However, this method does not give a detailed analysis of specific defect types, which makes the scalability of the method necessary to be further enhanced.…”

“…Stephen et al [13] complete the task of crack defect detection for tiles using seven-layer CNNs, but this study does not continue in-depth and is only conducted extensively for crack defects. Both Chaiyasarn et al [12] and Stephen [13] have conducted extensive studies on tile surface defects, but the methods are traditional deep learning models and have not been related to tile datasets with more complex textures and more types of defects. For the tiled dataset with complex texture multi-category defects, Wan et al [9] make a corresponding improvement to YOLOv5s by adding the attention mechanism CBAM [14] and adding an output prediction layer, which makes the improved model better for multi-category defect detection tasks.…”

A data-driven method for real-time compaction quality evaluation of a cement-stabilized base layer

“…The SVM uses a kernel function to map a low-dimensional inseparable features space into a high-dimensional space and seeks an optimal hyperplane to classify the space. Therefore, the type of kernel function selected has a significant influence on the learning effect (Chaiyasarn et al, 2021; Lu et al, 2021). The radial basis kernel function (RBF) was employed in this paper since it is widely used in SVM and commonly has good results (Zhang et al, 2019b).…”