Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Recognition and Visualization of Crack on Concrete Wall using Deep Learning and Transfer Learning

딥러닝과 전이학습을 이용한 콘크리트 균열 인식 및 시각화

  • Lee, Sang-Ik (Department of Rural Systems Engineering, Seoul National University) ;
  • Yang, Gyeong-Mo (Department of Rural Systems Engineering, Seoul National University) ;
  • Lee, Jemyung (Division of Environmental Science and Technology, Kyoto University) ;
  • Lee, Jong-Hyuk (Department of Rural Systems Engineering, Seoul National University) ;
  • Jeong, Yeong-Joon (Department of Rural Systems Engineering, Seoul National University) ;
  • Lee, Jun-Gu (Rural Research Institute, Korea Rural Community Corporation) ;
  • Choi, Won (Department of Rural Systems Engineering, Research Institute of Agriculture and Life Sciences, Seoul National University)
  • Received : 2019.02.28
  • Accepted : 2019.05.02
  • Published : 2019.05.31
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Abstract

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

Keywords

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Fig. 1 Example photo of cracked input data and non-cracked input data

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Fig. 2 Structure of Inception Model (Szegedy, 2015)

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Fig. 3 Examination data for crack visualization

Table 1 Examination data for crack recognition

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Table 2 Color by region of estimated probability from crack visualization

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Table 3 Accuracy of each model for each examination data

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Table 4 Result of crack visualization of 100px interval

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Table 5 Result of crack visualization of 50px interval

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Table 6 Result of crack visualization of 30px interval

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Table 7 Ratio of occurrence of false positive (FP) per estimation for interval, examination data and input data per category

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Table 8 Ratio of occurrence of false negative (FN) per estimation for interval, examination data and input data per category

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