TY - JOUR
T1 - Development of Automatic Inspection Systems for WRS2020 Plant Disaster Prevention Challenge Using Image Processing
AU - Shimizu, Yuya
AU - Kamegawa, Tetsushi
AU - Wang, Yongdong
AU - Tamura, Hajime
AU - Teshima, Taiga
AU - Nakano, Sota
AU - Tada, Yuki
AU - Nakano, Daiki
AU - Sasaki, Yuichi
AU - Sekito, Taiga
AU - Utsumi, Keisuke
AU - Nagao, Rai
AU - Semba, Mizuki
N1 - Funding Information:
This work was supported by the World Robot Summit Association Limited Liability Partnerships (WRSLLP).
Publisher Copyright:
© Fuji Technology Press Ltd.
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - auto inspec-tion
KW - image processing
KW - OCR
KW - WRS2020
KW - YOLO
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U2 - 10.20965/jrm.2023.p0065
DO - 10.20965/jrm.2023.p0065
M3 - Article
AN - SCOPUS:85148886052
SN - 0915-3942
VL - 35
SP - 65
EP - 73
JO - Journal of Robotics and Mechatronics
JF - Journal of Robotics and Mechatronics
IS - 1
ER -