Structural Monitoring and Maintenance

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Pixel-based crack image segmentation in steel structures using atrous separable convolution neural network

  • Ta, Quoc-Bao (Department of Ocean Engineering, Pukyong National University) ;
  • Pham, Quang-Quang (Department of Ocean Engineering, Pukyong National University) ;
  • Kim, Yoon-Chul (Department of Civil Engineering, Yonsei University) ;
  • Kam, Hyeon-Dong (Department of Ocean Engineering, Pukyong National University) ;
  • Kim, Jeong-Tae (Department of Ocean Engineering, Pukyong National University)
  • Received : 2022.07.09
  • Accepted : 2022.09.29
  • Published : 2022.09.25
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Abstract

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.

Keywords

Acknowledgement

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (2022R1A2C10038891161782064340101). The datasets used in this paper were granted by the committee of the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020). The authors would like to thank for the opportunity provided by IPC-SHM 2020.

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