Crack Detection in Concrete Structures Using Deep Learning

Abstract

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification

“…Recently, researchers have employed a combination of digital image processing schemes and DL for automated crack classification [ 54 , 55 , 56 , 57 ]. In [ 54 , 56 ], image processing was employed to assist DL, while [ 55 , 57 ] used DL to assist in image processing.…”

“…Recently, researchers have employed a combination of digital image processing schemes and DL for automated crack classification [ 54 , 55 , 56 , 57 ]. In [ 54 , 56 ], image processing was employed to assist DL, while [ 55 , 57 ] used DL to assist in image processing. Additionally, Kim et al [ 54 ] used image processing to extract potential crack candidate regions (CCRs) and filter background features, thereby reducing the volume of data required for the DL model.…”

“…Additionally, Kim et al [ 54 ] used image processing to extract potential crack candidate regions (CCRs) and filter background features, thereby reducing the volume of data required for the DL model. Golding et al [ 56 ] transformed RGB images to grayscale before DL implementation using a pretrained VGG16 architecture, thereby reducing the computation associated with different input channels. Nomura et al [ 55 ] and Yu et al [ 57 ] first extracted bounding boxes of cracks using YOLO-based object detection.…”

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

“…Deep Convolutional Neural Networks (DCNN) are one of the most dependable deep learning technologies [9,10]. The potential causes of cracks like temperature, moisture and other durability behavior in concrete structures are also considered and play a significant role in determining the size of damage detected using CNN [11]. Some researchers have proposed alternative methods, such as RNN, while many deep learning (DL) models, including CNN, use larger datasets to improve their results.…”

Role of Convolutional Neural Networks to Diagnosis Medical Images

“…In trials on depression evaluation based on publicly available data, the depression control group and the healthy control group had accuracy, sensitivity, and specificity of 99.08 percent, 98.77 percent, and 99.42 percent, respectively. A convolutional neural network; deep learning based autonomous crack detection system has been proposed by Golding VP et al [4] Before training a pretrained VGG16 architecture to construct several CNN models, 40,000 RGB images were processed to improve the CNN classification performance for increased pixel segmentation. This concept therefore affects automatic crack detection of concrete constructions and the increased dependability of the data collected.…”