Title: GM-YOLOV8-based safety hazard detection method in power construction

Authors: Entie Qi; Jialong Ge; Liying Zhao; Hongxia Ni; Cheng Li; Dianzhi Chen; Sinan Shi

Addresses: School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China ' School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China ' School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China ' School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China ' School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China ' School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China ' School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun City 130000, China

Abstract: In power construction settings, the operation of heavy machinery and the risk of fire present substantial hazards to the safety of transmission lines. There is an urgent need for real-time surveillance of potential safety threats during the construction process. This paper proposes a generalised multi-scale-YOLOv8 (GM-YOLOv8) hazard detection algorithm. This algorithm introduces the reparameterised generalised feature pyramid network (RepGFPN) that improves the model's capacity to capture overarching patterns and fine-grained details. It also introduces a multi-scale cross-axis attention module (MCA). This module enhances the network's representational capabilities and improving the detection of distant hazards. Additionally, the adoption of the Powerful-IOU loss function, which includes a non-monotonic focus mechanism, enhances the model by adaptively penalizing object size and modulating gradients based on anchor box quality. Compared to a lightweight YOLOv8 model (YOLOv8n) algorithm, GM-YOLOv8 enhances detection precision by 5.3%, accuracy by 6.8%, and recall by 6.4%, ensuring improved safety in construction environments.

Keywords: safety hazard detection; power construction; YOLOv8; reparameterised generalised feature pyramid network; RepGFPN; multi-scale cross-axis attention module; MCA; PIOU.

DOI: 10.1504/IJSNET.2025.144628

International Journal of Sensor Networks, 2025 Vol.47 No.3, pp.162 - 172

Received: 11 Sep 2024
Accepted: 30 Sep 2024
Published online: 25 Feb 2025 *

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