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Open AccessArticle

Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network

1
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
2
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
3
Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2069; https://doi.org/10.3390/s20072069
Received: 17 January 2020 / Revised: 13 March 2020 / Accepted: 2 April 2020 / Published: 7 April 2020
Crack detection on dam surfaces is an important task for safe inspection of hydropower stations. More and more object detection methods based on deep learning are being applied to crack detection. However, most of the methods can only achieve the classification and rough location of cracks. Pixel-level crack detection can provide more intuitive and accurate detection results for dam health assessment. To realize pixel-level crack detection, a method of crack detection on dam surface (CDDS) using deep convolution network is proposed. First, we use an unmanned aerial vehicle (UAV) to collect dam surface images along a predetermined trajectory. Second, raw images are cropped. Then crack regions are manually labelled on cropped images to create the crack dataset, and the architecture of CDDS network is designed. Finally, the CDDS network is trained, validated and tested using the crack dataset. To validate the performance of the CDDS network, the predicted results are compared with ResNet152-based, SegNet, UNet and fully convolutional network (FCN). In terms of crack segmentation, the recall, precision, F-measure and IoU are 80.45%, 80.31%, 79.16%, and 66.76%. The results on test dataset show that the CDDS network has better performance for crack detection of dam surfaces. View Full-Text
Keywords: crack detection; dam surface; UAV; pixel-level; deep convolutional network crack detection; dam surface; UAV; pixel-level; deep convolutional network
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MDPI and ACS Style

Feng, C.; Zhang, H.; Wang, H.; Wang, S.; Li, Y. Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network. Sensors 2020, 20, 2069. https://doi.org/10.3390/s20072069

AMA Style

Feng C, Zhang H, Wang H, Wang S, Li Y. Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network. Sensors. 2020; 20(7):2069. https://doi.org/10.3390/s20072069

Chicago/Turabian Style

Feng, Chuncheng; Zhang, Hua; Wang, Haoran; Wang, Shuang; Li, Yonglong. 2020. "Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network" Sensors 20, no. 7: 2069. https://doi.org/10.3390/s20072069

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