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Research on Improving the Safety of Construction Workers FPN-PAN-YOLOv5s Method for Crack Detection in Glass Melting Furnace Crowns
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Author(s): Yingjun Feng (Henan Geology Mineral College, China), Yang Mei (Henan Technical College of Construction, China), Zhixiang Wang (Henan Province Installation Group Co., Ltd., China), Guohua Yang (Henan Geology Mineral College, China)and Nianbo Li (Henan Province Installation Group Co., Ltd., China)
Copyright: 2025
Volume: 17
Issue: 1
Pages: 17
Source title:
International Journal of Interdisciplinary Telecommunications and Networking (IJITN)
Editor(s)-in-Chief: Efosa Carroll Idemudia (Howard University, USA)
DOI: 10.4018/IJITN.375627
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Abstract
To address the shortcomings of existing methods and better detect various cracks appearing on the crown of glass melting furnaces, a network model in the “You Only Look Once” (YOLO) series, YOLOv5s, was used to extract features from furnace arch images. To address the issue of insufficient utilization of shallow feature information in this method, a path aggregation algorithm was adopted to optimize YOLOv5s based on a feature pyramid network (FPN). Then, a furnace arch crack detection method, based on an FPN-path aggregation network (PAN)-YOLOv5s, was constructed. The results indicate that the crack detection method for a glass furnace roof based on the FPN-PAN-YOLOv5s can achieve crack recognition in complex environments, providing effective support for the safety detection of glass furnace roofs and ensuring the safe operation of workshop workers.
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