Development of Automatic Inspection Systems for WRS2020 Plant Disaster Prevention Challenge Using Image Processing

Yuya Shimizu, Tetsushi Kamegawa, Yongdong Wang, Hajime Tamura, Taiga Teshima, Sota Nakano, Yuki Tada, Daiki Nakano, Yuichi Sasaki, Taiga Sekito, Keisuke Utsumi, Rai Nagao, Mizuki Semba

Research output: Contribution to journalArticlepeer-review


In this article, an approach used for the inspection tasks in the WRS2020 Plant Disaster Prevention Challenge is explained. The tasks were categorized into three categories: reading pressure gauges, inspecting rust on a tank, and inspecting cracks in a tank. For reading pressure gauges, the “you only look once” algorithm was used to focus on a specific pressure gauge and check the pressure gauge range strings on the gauge using optical character recognition algorithm. Finally, a previously learned classifier was used to read the values shown in the gauge. For rust inspection, image processes were used to focus on a target plate that may be rusted for rust detection. In particular, it was necessary to report the rust area and distribution type. Thus, the pixel ratio and grouping of rust were used to count the rust. The approach for crack inspection was similar to that for rust. The target plate was focused on first, and then the length of the crack was measured using image processing. Its width was not measured but was calculated using the crack area and length. For each system developed to approach each task, the results of the preliminary experiment and those of WRS2020 are shown. Finally, the ap-proaches are summarized, and planned future work is discussed.

Original languageEnglish
Pages (from-to)65-73
Number of pages9
JournalJournal of Robotics and Mechatronics
Issue number1
Publication statusPublished - Feb 2023


  • OCR
  • WRS2020
  • YOLO
  • auto inspec-tion
  • image processing

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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