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http://dx.doi.org/10.9708/jksci.2022.27.09.033

Abnormality Detection Method of Factory Roof Fixation Bolt by Using AI  

Kim, Su-Min (Epozen's research institute)
Sohn, Jung-Mo (Epozen's research institute)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.9, 2022 , pp. 33-40 More about this Journal
Abstract
In this paper, we propose a system that analyzes drone photographic images of panel-type factory roofs and conducts abnormal detection of bolts. Currently, inspectors directly climb onto the roof to carry out the inspection. However, safety accidents caused by working conditions at high places are continuously occurring, and new alternatives are needed. In response, the results of drone photography, which has recently emerged as an alternative to the dangerous environment inspection plan, will be easily inspected by finding the location of abnormal bolts using deep learning. The system proposed in this study proceeds with scanning the captured drone image using a sample image for the situation where the bolt cap is released. Furthermore, the scanned position is discriminated by using AI, and the presence/absence of the bolt abnormality is accurately discriminated. The AI used in this study showed 99% accuracy in test results based on VGGNet.
Keywords
AI; Image scanning; Anomaly detection; VGGNet; Roof bolt;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Lee, Youg-Soon, "Safety of Roof Work for Field Workers," Health and Safety Corporation Education Media Office, pp. 5-13, 2016.
2 Choe, Don-Hong, "A Study on the Actual Condition of Falling Disaster and Safety Model in Roof Work," Serial No. 291, Korea Industrial Health Association, pp. 72-73, 2012.
3 Kim, Hyejin, Kim, Jiyoung , Kim, Yongil, "A Study on the Object-based Building Extraction Using UAV Imagery," journal of Korean Society for Geospatial Information Science, Vol. 27, No. 4, pp. 21-28, 2019. DOI: http://dx.doi.org/10.7319/kogsis.2019.27.4.021   DOI
4 Je-Hwan Ryu, Min-Oh Heo, Byoung-Tak Zhang, "CNN-based Classification Methods for Filtering of Defect Pixel Circuit Images," Korean Institute of Information Scientists and Engineers, pp. 907-909, Dec 2017.
5 Kim, Do-Su, Shin, Yoon-Seok, "A study on the Importance of Risk Factor for Falling Accident in Construction Site," The Korea Institute of Building Construction, Vol. 18, No. 2, pp. 63-65, Korea, Nov 2018.
6 J.-W. Kim, Y.-W. Jung, and H.-C. Rhim, "Study on Structure Visual Inspection Technology using Drones and Image Techniques," Journal of the Korea Institute of Building Construction, Vol. 17, No. 6, pp. 545-557, Dec 2017. DOI: https://doi.org/10.5345/JKIBC.2017.17.6.545   DOI
7 Karen Simonyan, Andrew Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," IEEE International Conference on Computer Vision (ICCV), Dec 2015. DOI: https://doi.org/10.48550/arxiv.1409.1556   DOI
8 Youngjun Yoo, Daehee Kim, Myunghak Lee, Woong Kang, Jaekoo Lee, "Underground Cavity Detection using Deep Neural Network(DNN) Trained on Simulated Data," Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp. 593-594, Aug 2020.
9 Seung Hyun Paik,, Daeyoung Choi, Young-Kyu Kim, SangWoo Jung, Dae-Nyeon Kim, "Implementation of the Drones with Deep-Learning Crack Detection Analysis for Inspection of Bridge," Journal of KIIT, Vol. 19, No. 3, pp. 45-52, Mar 2021. DOI : 10.14801/jkiit.2021.19.3.45   DOI
10 Shin, Jung-Il, Kim, Ik-Jae, Hwang, Dong-Hyun, Lee, Jong-Min, and Lim, Seong-Ha, "Availability Analysis on Detection of Small Scale Gas Emission Facilities using Drone Imagery," Journal of Cadastre & Land InformatiX, Vol. 47, No. 1, pp. 213-223, Jun. 2017. DOI: https://doi.org/10.22640/lxsiri.2017.47.1.213   DOI
11 Choi, Won-Ho, "Design and Implementation of PCB Defect Detection System using Deep Learning," M.S. thesis, Electrical Engineering and Computer Science, Hanyang Univ., Korea, 2022.
12 Seung Heon Han, Tariq Rahim, Jae Han Park, Soo Young Shin, "Damaged solar panel detection drone system using deep learning and thermal imaging camera," Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp. 899-900, 2020.
13 Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," Proceeding of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226-231, 1996.
14 Sklearn.cluster.DBSCAN, https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html