Robust Concrete Crack Detection Using Deep Learning-Based Semantic Segmentation

2019 ◽  
Vol 20 (1) ◽  
pp. 287-299 ◽  
Author(s):  
Donghan Lee ◽  
Jeongho Kim ◽  
Daewoo Lee
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Semantic Segmentation ◽  
Concrete Crack

A research on an improved Unet-based concrete crack detection algorithm

2020 ◽  
pp. 147592172094006
Author(s):  
Lingxin Zhang ◽  
Junkai Shen ◽  
Baijie Zhu
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Important Indicator ◽  
Test Dataset ◽  
Training Time ◽  
Damage Level ◽  
Detection Algorithms ◽  
Concrete Crack ◽  
Model Affect

Crack is an important indicator for evaluating the damage level of concrete structures. However, traditional crack detection algorithms have complex implementation and weak generalization. The existing crack detection algorithms based on deep learning are mostly window-level algorithms with low pixel precision. In this article, the CrackUnet model based on deep learning is proposed to solve the above problems. First, crack images collected from the lab, earthquake sites, and the Internet are resized, labeled manually, and augmented to make a dataset (1200 subimages with 256 × 256 × 3 resolutions in total). Then, an improved Unet-based method called CrackUnet is proposed for automated pixel-level crack detection. A new loss function named generalized dice loss is adopted to detect cracks more accurately. How the size of the dataset and the depth of the model affect the training time, detecting accuracy, and speed is researched. The proposed methods are evaluated on the test dataset and a previously published dataset. The highest results can reach 91.45%, 88.67%, and 90.04% on test dataset and 98.72%, 92.84%, and 95.44% on CrackForest Dataset for precision, recall, and F1 score, respectively. By comparing the detecting accuracy, the training time, and the information of datasets, CrackUnet model outperform than other methods. Furthermore, six images with complicated noise are used to investigate the robustness and generalization of CrackUnet models.

Quantify pixel-level detection of dam surface crack using deep learning

2022 ◽  
Author(s):  
Bo Chen ◽  
Hua Zhang ◽  
Yonglong Li ◽  
Shuang Wang ◽  
Huaifang Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Crack ◽  
Crack Detection ◽  
State Of The Art ◽  
Contextual Information ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Cross Entropy ◽  
Detection Methods ◽  
Water Conservancy

Abstract An increasing number of detection methods based on computer vision are applied to detect cracks in water conservancy infrastructure. However, most studies directly use existing feature extraction networks to extract cracks information, which are proposed for open-source datasets. As the cracks distribution and pixel features are different from these data, the extracted cracks information is incomplete. In this paper, a deep learning-based network for dam surface crack detection is proposed, which mainly addresses the semantic segmentation of cracks on the dam surface. Particularly, we design a shallow encoding network to extract features of crack images based on the statistical analysis of cracks. Further, to enhance the relevance of contextual information, we introduce an attention module into the decoding network. During the training, we use the sum of Cross-Entropy and Dice Loss as the loss function to overcome data imbalance. The quantitative information of cracks is extracted by the imaging principle after using morphological algorithms to extract the morphological features of the predicted result. We built a manual annotation dataset containing 1577 images to verify the effectiveness of the proposed method. This method achieves the state-of-the-art performance on our dataset. Specifically, the precision, recall, IoU, F1_measure, and accuracy achieve 90.81%, 81.54%, 75.23%, 85.93%, 99.76%, respectively. And the quantization error of cracks is less than 4%.

scholarly journals Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4251 ◽  
Author(s):  
M. M. Manjurul Islam ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Main Idea ◽  
Semantic Segmentation ◽  
Civil Infrastructure ◽  
Global Context ◽  
Concrete Crack ◽  
Common Trend ◽  
Convolutional Encoder

The visual inspection of massive civil infrastructure is a common trend for maintaining its reliability and structural health. However, this procedure, which uses human inspectors, requires long inspection times and relies on the subjective and empirical knowledge of the inspectors. To address these limitations, a machine vision-based autonomous crack detection method is proposed using a deep convolutional neural network (DCNN) technique. It consists of a fully convolutional neural network (FCN) with an encoder and decoder framework for semantic segmentation, which performs pixel-wise classification to accurately detect cracks. The main idea is to capture the global context of a scene and determine whether cracks are in the image while also providing a reduced and essential picture of the crack locations. The visual geometry group network (VGGNet), a variant of the DCCN, is employed as a backbone in the proposed FCN for end-to-end training. The efficacy of the proposed FCN method is tested on a publicly available benchmark dataset of concrete crack images. The experimental results indicate that the proposed method is highly effective for concrete crack classification, obtaining scores of approximately 92% for both the recall and F1 average.

Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

2019 ◽  
Vol 18 (5-6) ◽  
pp. 1722-1737 ◽  
Author(s):  
Keunyoung Jang ◽  
Namgyu Kim ◽  
Yun-Kyu An
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Large Scale ◽  
Crack Detection ◽  
Continuous Wave ◽  
Imaging System ◽  
Concrete Specimen ◽  
Crack Identification ◽  
False Alarms ◽  
Concrete Crack

This article proposes a deep learning–based autonomous concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared thermography images are able to improve crack detectability while minimizing false alarms. In particular, large-scale concrete-made infrastructures such as bridge and dam can be effectively inspected by spatially scanning the unmanned vehicle–mounted hybrid imaging system including a vision camera, an infrared camera, and a continuous-wave line laser. However, the expert-dependent decision-making for crack identification which has been widely used in industrial fields is often cumbersome, time-consuming, and unreliable. As a target concrete structure gets larger, automated decision-making becomes more desirable from the practical point of view. The proposed technique is able to achieve automated crack identification and visualization by transfer learning of a well-trained deep convolutional neural network, that is, GoogLeNet, while retaining the advantages of the hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen with cracks of various sizes. The test results reveal that macro- and microcracks are automatically visualized while minimizing false alarms.

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