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Autonomous Positioning of Fall Protection Anchor Points for Transmission Towers Based on Dynamic Feature Fusion and Structural Priors.

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Title: Autonomous Positioning of Fall Protection Anchor Points for Transmission Towers Based on Dynamic Feature Fusion and Structural Priors.
Authors: Peng, Yu1 3366053979@qq.com, Yang, Chunqing2 253430266@qq.com, Chen, Jiahui3 cjh13w@163.com, Wan, Li4 wan23li@163.com, Yu, Jian5 921832741@qq.com, Qiu, Siyu4 412582182@qq.com, Wang, Gang4 425053615@qq.com
Source: IAENG International Journal of Computer Science. May2026, Vol. 53 Issue 5, p1925-1936. 12p.
Subjects: Object recognition (Computer vision), Structural analysis (Engineering), Accidental fall prevention, Feature extraction, Utility poles, Industrial safety, Drone aircraft
Abstract: Manual installation of fall protection anchor points on transmission towers is labor-intensive and presents significant safety risks for workers at high altitudes. To address these challenges, this paper proposes an autonomous positioning and installation system that leverages UAV visual perception and autonomous decision-making. Initially, an enhanced YOLOv8 model is developed by incorporating a lightweight FasterC2f module, a CBAM attention mechanism, and a Dynamic Multi-Scale Feature Fusion Module, which substantially improves detection accuracy and robustness in complex environments. Additionally, an intelligent decision-making algorithm based on structural symmetry analysis is introduced. This algorithm employs symmetry axis estimation, node pair matching, and multi-dimensional scoring to automatically identify the installation point with optimal mechanical performance. Moreover, a multi-coordinate transformation model is created to accurately map image pixels to three-dimensional world coordinates. Experimental results indicate that the proposed method achieves an average node detection accuracy (mAP@50) of 94.2% and an anchor point positioning error of 6.5 pixels. The time required for single-point installation is reduced from 30 minutes to 6 minutes, enhancing efficiency by 400-500% and mitigating the risks associated with high-altitude climbing. This research offers a viable technical solution for intelligent safety protection in tower maintenance. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Manual installation of fall protection anchor points on transmission towers is labor-intensive and presents significant safety risks for workers at high altitudes. To address these challenges, this paper proposes an autonomous positioning and installation system that leverages UAV visual perception and autonomous decision-making. Initially, an enhanced YOLOv8 model is developed by incorporating a lightweight FasterC2f module, a CBAM attention mechanism, and a Dynamic Multi-Scale Feature Fusion Module, which substantially improves detection accuracy and robustness in complex environments. Additionally, an intelligent decision-making algorithm based on structural symmetry analysis is introduced. This algorithm employs symmetry axis estimation, node pair matching, and multi-dimensional scoring to automatically identify the installation point with optimal mechanical performance. Moreover, a multi-coordinate transformation model is created to accurately map image pixels to three-dimensional world coordinates. Experimental results indicate that the proposed method achieves an average node detection accuracy (mAP@50) of 94.2% and an anchor point positioning error of 6.5 pixels. The time required for single-point installation is reduced from 30 minutes to 6 minutes, enhancing efficiency by 400-500% and mitigating the risks associated with high-altitude climbing. This research offers a viable technical solution for intelligent safety protection in tower maintenance. [ABSTRACT FROM AUTHOR]
ISSN:1819656X