Research on Improving the Safety of Construction Workers FPN-PAN-YOLOv5s Method for Crack Detection in Glass Melting Furnace Crowns

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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Publication Details

Journal
International Journal of Interdisciplinary Telecommunications and Networking
Published
2025-05-07
DOI
https://doi.org/10.4018/ijitn.375627
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.24
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article

Research on Improving the Safety of Construction Workers FPN-PAN-YOLOv5s Method for Crack Detection in Glass Melting Furnace Crowns

Yingjun Feng, Nianbo Li, Zhixiang Wang, Guohua Yang et al.
1 citations
International Journal of Interdisciplinary Telecommunications and Networking
Industrial Vision Systems and Defect Detection
1.24
article

Research on Improving the Safety of Construction Workers FPN-PAN-YOLOv5s Method for Crack Detection in Glass Melting Furnace Crowns

Yingjun Feng, Nianbo Li, Zhixiang Wang, Guohua Yang, Yang Mei
article en
1 citations

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.

International Journal of Interdisciplinary Telecommunications and NetworkingVol. 17(1)
Henan Forestry Vocational College (CN)
Openalex Percentile: Top 20%
Industrial Vision Systems and Defect Detection
1.24
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