Smart Structures and Systems

  • 제27권6호
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  • Pages.1031-1040
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  • 2021
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A deep learning-based vision enhancement method for UAV assisted visual inspection of concrete cracks

  • Qi, Yanzhi (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Yuan, Cheng (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Kong, Qingzhao (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Xiong, Bing (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University) ;
  • Li, Peizhen (Department of Disaster Mitigation for Structures, Tongji University)
  • 투고 : 2020.11.13
  • 심사 : 2021.03.17
  • 발행 : 2021.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Implementing unmanned aerial vehicles (UAVs) on concrete surface-crack inspection leads to a promising visual crack detection approach. One of the challenges for automated field visual cracking inspection is image degradation caused by the rain or fog and motion blur during data acquisition. The present study combines two deep neural networks to address the image degradation problem. By using the Variance of Laplacian algorithm for quantifying image clarity, the proposed deep neural networks can remarkably enhance the sharpness of the degraded images. After vision enhancement process, Mask Region Convolutional Neutral Network (Mask R-CNN) was developed to perform automated crack identification and segmentation. Results show a 8~13% enhancement in prediction accuracy compared to the degraded images, indicating that the proposed deep learning-based vision enhancement method can effectivey identify and segment concrete surface cracks from photos captured by UAVs.

키워드

과제정보

This research is supported by the National Key Research and Development Program of China (Grant No. 2018YFC0705602), Science and Technology Commission of Shanghai Municipality (STCSM) (Grant No. 19DZ1201200), and China National Science Foundation (Grant No. 51978507).

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