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        <title type="main" level="a">iSafe Welding System: Computer Vision-Based Monitoring System for Safe Welding Work</title>
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          <persName n="1" ref="https://orcid.org/0000-0003-2257-290X" type="ORCID">
            <forename>Syed Farhan Alam</forename>
            <surname>Zaidi</surname>
            <placeName type="affiliation">Chung Ang University, Korea, Republic of</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0002-6909-5189" type="ORCID">
            <forename>Rahat</forename>
            <surname>Hussain</surname>
            <placeName type="affiliation">Chung Ang University, Korea, Republic of</placeName>
          </persName>
          <persName n="3">
            <forename>Muhammad Sibtain</forename>
            <surname>Abbas</surname>
            <placeName type="affiliation">Chung Ang University, Korea, Republic of</placeName>
          </persName>
          <persName n="4" ref="https://orcid.org/0000-0002-8192-340X" type="ORCID">
            <forename>Jaehun</forename>
            <surname>Yang</surname>
            <placeName type="affiliation">Chung Ang University, Korea, Republic of</placeName>
          </persName>
          <persName n="5" ref="https://orcid.org/0000-0002-6559-8782" type="ORCID">
            <forename>Doyeop</forename>
            <surname>Lee</surname>
            <placeName type="affiliation">Chung Ang University, Korea, Republic of</placeName>
          </persName>
          <persName n="6" ref="https://orcid.org/0000-0003-2256-300X" type="ORCID">
            <forename>Chansik</forename>
            <surname>Park</surname>
            <placeName type="affiliation">Chung Ang University, Korea, Republic of</placeName>
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          <resp>This is a section of <title>CONVR 2023 - Proceedings of the 23rd International Conference on  Construction Applications of Virtual Reality </title>(DOI: <idno type="DOI">10.36253/979-12-215-0289-3</idno>) by </resp>
          <name>Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi</name>
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        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2023">2023</date>
        <idno type="DOI">https://doi.org/10.36253/10.36253/979-12-215-0289-3.66</idno>
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          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
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            <p>Content licence CC BY-NC 4.0</p>
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      <abstract xml:lang="en">
        <p>The construction industry faces significant challenges, including a high prevalence of occupational incidents, often involving fires, explosions, and burn-related accidents due to worker non-compliance with safety protocols. Adherence to safety guidelines and proper utilization of safety equipment are critical to preventing such incidents and safeguarding workers in hazardous work environments. Consequently, a monitoring system tailored for construction safety during welding operations becomes imperative to mitigate the risk of fire accidents. This paper conducts a brief analysis of OSHA rules pertaining to welding work and introduces the iSafe Welding system, an advanced real-time safety monitoring and compliance enforcement solution designed specifically for construction site welding operations. Harnessing the real-time object detection algorithm YOLOv7 in conjunction with rule-based scene classification, the system excels in identifying potential safety violations. Rigorous evaluation, encompassing precision, recall, mean Average Precision (mAP), accuracy, and the F1-Score, sheds light on its strengths and areas for improvement. The system showcases robust performance in rule-based scene classification, achieving high accuracy, precision, and recall rates. Notably, the iSafe Welding system demonstrates a formidable potential for enhancing construction site safety and regulatory compliance. Ongoing enhancements, including dataset expansion and model refinement, underscore its commitment to real-world deployment and its strength in ensuring worker safety</p>
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        <keywords>
          <list>
            <item>Safety monitoring</item>
            <item>scene classification</item>
            <item>welding work</item>
            <item>fire prevention</item>
            <item>construction safety</item>
            <item>OSHA rules compliance</item>
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      <p>It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.66<ref target="https://doi.org/10.36253/10.36253/979-12-215-0289-3.66" /></p>
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          <head>References</head>
          <bibl n="137061">
            <bibl>Chen, S., Yang, D., Liu, J., Tian, Q., &amp;amp; Zhou, F. (2023). Automatic weld type classification, tacked spot recognition and weld ROI determination for robotic welding based on modified YOLOv5. Robotics and Computer-Integrated Manufacturing, 81, 102490.</bibl>
            <idno type="DOI">10.1016/j.rcim.2022.102490</idno>
          </bibl>
          <bibl n="138999">
            <bibl>Chen, W., Li, C., &amp;amp; Guo, H. (2023). A lightweight face-assisted object detection model for welding helmet use. Expert Systems with Applications, 221, 119764.</bibl>
            <idno type="DOI">10.1016/j.eswa.2023.119764</idno>
          </bibl>
          <bibl n="138133">
            <bibl>Diwan, T., Anirudh, G., &amp;amp; Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243–9275.</bibl>
            <idno type="DOI">10.1007/s11042-022-13644-y</idno>
          </bibl>
          <bibl n="136629">Hussain, R., Zaidi, S. F. A., Pedro, A., Abbas, M. S., Pyeon, J.-H., &amp;amp; Park, C. (2022). Conceptual Framework for Safety Training for Migrant Construction Workers using Virtual Reality Techniques. 22nd International Conference on Construction Applications of Virtual Reality (CONVR2022), 1162–1168. https://www.researchgate.net/publication/366004620</bibl>
          <bibl n="137134">Jeong, J., Han, S., &amp;amp; Kang, L. (2017). Development of construction site monitoring system using UAV data for civil engineering project. Korean Journal of Construction …, 18(5), 41–49. http://www.koreascience.or.kr/article/JAKO201730049612290.page</bibl>
          <bibl n="138393">JOHN, M. (2023). A Study on Welding Bead Detection and Inspection Using Computer Vision Algorithms [Pukyong National University]. https://repository.pknu.ac.kr:8443/handle/2021.oak/32898</bibl>
          <bibl n="138199">
            <bibl>Khan, N., Zaidi, S. F. A., Yang, J., Park, C., &amp;amp; Lee, D. (2023). Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology. Buildings, 13(8), 2093.</bibl>
            <idno type="DOI">10.3390/buildings13082093</idno>
          </bibl>
          <bibl n="138075">
            <bibl>Liu, H., Tian, Y., Li, L., Lu, Y., Feng, J., &amp;amp; Xi, F. (2023). Full-cycle data purification strategy for multi-type weld seam classification with few-shot learning. Computers in Industry, 150, 103939.</bibl>
            <idno type="DOI">10.1016/j.compind.2023.103939</idno>
          </bibl>
          <bibl n="136825">
            <bibl>Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., &amp;amp; Berg, A. C. (2016). SSD: Single shot multibox detector. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9905 LNCS, 21–37.</bibl>
            <idno type="DOI">10.1007/978-3-319-46448-0_2</idno>
          </bibl>
          <bibl n="137062">Long, X., Deng, K., Wang, G., Zhang, Y., Dang, Q., Gao, Y., Shen, H., Ren, J., Han, S., Ding, E., &amp;amp; Wen, S. (2020). PP-YOLO: An Effective and Efficient Implementation of Object Detector. ArXiv Preprint ArXiv:2007.12099. http://arxiv.org/abs/2007.12099</bibl>
          <bibl n="139431">
            <bibl>Nill, R. J. (2019). How to select and use personal protective equipment. Handbook of Occupational Safety and Health, 468–494.</bibl>
            <idno type="DOI">10.1002/9781119581482.ch15</idno>
          </bibl>
          <bibl n="136803">
            <bibl>Ramadan, A. A. A., Hussein, H. M. A., Mazloum, A. G., Sakr, S. S., &amp;amp; Naranje, V. (2023). Computer System for Detection and Classification of Welding Defects. Proceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023, 316–319.</bibl>
            <idno type="DOI">10.1109/ICCIKE58312.2023.10131694</idno>
          </bibl>
          <bibl n="137221">
            <bibl>Wang, C.-Y., Bochkovskiy, A., &amp;amp; Liao, H.-Y. M. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7464–7475.</bibl>
            <idno type="DOI">10.1109/cvpr52729.2023.00721</idno>
          </bibl>
          <bibl n="137904">
            <bibl>Wu, Z., Gao, P., Han, J., Bai, L., Lu, J., &amp;amp; Zhao, Z. (2023). Real-time segmentation network for accurate weld detection in large weldments. Engineering Applications of Artificial Intelligence, 117, 105008.</bibl>
            <idno type="DOI">10.1016/j.engappai.2022.105008</idno>
          </bibl>
          <bibl n="137266">
            <bibl>Xu, H., Liu, Y., Shu, C. M., Bai, M., Motalifu, M., He, Z., Wu, S., Zhou, P., &amp;amp; Li, B. (2022). Cause analysis of hot work accidents based on text mining and deep learning. Journal of Loss Prevention in the Process Industries, 76, 104747.</bibl>
            <idno type="DOI">10.1016/j.jlp.2022.104747</idno>
          </bibl>
          <bibl n="136976">
            <bibl>Yang, L., Li, E., Long, T., Fan, J., Mao, Y., Fang, Z., &amp;amp; Liang, Z. (2018). A welding quality detection method for arc welding robot based on 3D reconstruction with SFS algorithm. International Journal of Advanced Manufacturing Technology, 94(1–4), 1209–1220.</bibl>
            <idno type="DOI">10.1007/s00170-017-0991-9</idno>
          </bibl>
        </listBibl>
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