scholarly journals Crack Detection from a Concrete Surface Image Based on Semantic Segmentation Using Deep Learning

2020 ◽  
Vol 18 (9) ◽  
pp. 493-504 ◽  
Author(s):  
Tatsuro Yamane ◽  
Pang-jo Chun
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Semantic Segmentation ◽  
Concrete Surface ◽  
Surface Image

scholarly journals Autonomous Crack and Bughole Detection for Concrete Surface Image Based on Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yujia Sun ◽  
Yang Yang ◽  
Gang Yao ◽  
Fujia Wei ◽  
Mingpu Wong
Keyword(s):  
Deep Learning ◽  
Concrete Surface ◽  
Surface Image

scholarly journals DEEP LEARNING AND RANDOM FOREST BASED CRACK DETECTION FROM AN IMAGE OF CONCRETE SURFACE

2017 ◽  
Vol 73 (2) ◽  
pp. I_297-I_307 ◽  
Author(s):  
Pang-jo CHUN ◽  
Yuri SHIMAMOTO ◽  
Kazuaki OKUBO ◽  
Chihiro MIWA ◽  
Mitao OHGA
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Crack Detection ◽  
Concrete Surface

scholarly journals Concrete Cracks Detection Based on FCN with Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2686 ◽  
Author(s):  
Jianming Zhang ◽  
Chaoquan Lu ◽  
Jin Wang ◽  
Lei Wang ◽  
Xiao-Guang Yue
Keyword(s):  
Crack Detection ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Concrete Surface ◽  
Input Image ◽  
Feature Maps ◽  
Test Set ◽  
Dilated Convolution ◽  
Fully Convolutional Networks ◽  
Segmentation Task

In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the input image, designed the dilated convolutions with different dilation rates to extract the feature maps of different receptive fields, and fused the extracted features from multiple branches. Then, we exploited the stacked deconvolution to do up-sampling operator in the fused feature maps. Finally, we used the SoftMax function to classify the feature maps at the pixel level. In order to verify the validity of the model, we introduced the commonly used evaluation indicators of semantic segmentation: Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU). The experimental results show that the proposed model converges faster and has better generalization performance on the test set by introducing dilated convolutions with different dilation rates and a multi-branch fusion strategy. Our model has a PA of 96.84%, MPA of 92.55%, MIoU of 86.05% and FWIoU of 94.22% on the test set, which is superior to other 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 Deep-Learning-Based Bughole Detection for Concrete Surface Image

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Gang Yao ◽  
Fujia Wei ◽  
Yang Yang ◽  
Yujia Sun
Keyword(s):  
Deep Learning ◽  
Casting Process ◽  
Concrete Surface ◽  
Input Image ◽  
Surface Imperfections ◽  
Color Differences ◽  
Otsu Method ◽  
Surface Image ◽  
Image Test ◽  
The Comparative Study

Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real-world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color-differences, and nonuniform illumination on the concrete surface.

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