Research on fall prevention and safety protection for electric power tower climbing operations based on the longhorn whisker algorithm

Abstract With the continuous development of the power industry, the safety of power tower climbing operations is increasingly valued. This study focuses on the anti-fall safety protection of electric power tower climbing operations and proposes a study on the anti-fall safety protection of electric power tower climbing operations based on the Longhorn whisker algorithm. Firstly, real-time image acquisition of the climbing operation site is carried out through the camera equipment carried by the drone, ensuring clear and comprehensive information on the working environment is obtained. Secondly, extract hazardous features in climbing operations, such as abnormal worker posture, tool detachment, etc. These features are important indicators of potential fall risks. Finally, a fall hazard detection model is constructed and optimized using the Longhorn Whisker algorithm to identify and warn of impending fall events, thereby providing timely safety protection for climbing operators. The experimental results show that this method has shown significant effectiveness in improving job safety, providing strong technical support for the safety protection of power tower climbing operations.

Authors

Institutions

Publication Details

Journal
Journal of Physics Conference Series
Published
2024-11-01
DOI
https://doi.org/10.1088/1742-6596/2876/1/012016
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on fall prevention and safety protection for electric power tower climbing operations based on the longhorn whisker algorithm

Peng Zhan, Shuang Yu, Yonghao Zeng
Journal of Physics Conference Series
Power Systems and Technologies
article

Research on fall prevention and safety protection for electric power tower climbing operations based on the longhorn whisker algorithm

Peng Zhan, Shuang Yu, Yonghao Zeng
article en

Abstract

Abstract With the continuous development of the power industry, the safety of power tower climbing operations is increasingly valued. This study focuses on the anti-fall safety protection of electric power tower climbing operations and proposes a study on the anti-fall safety protection of electric power tower climbing operations based on the Longhorn whisker algorithm. Firstly, real-time image acquisition of the climbing operation site is carried out through the camera equipment carried by the drone, ensuring clear and comprehensive information on the working environment is obtained. Secondly, extract hazardous features in climbing operations, such as abnormal worker posture, tool detachment, etc. These features are important indicators of potential fall risks. Finally, a fall hazard detection model is constructed and optimized using the Longhorn Whisker algorithm to identify and warn of impending fall events, thereby providing timely safety protection for climbing operators. The experimental results show that this method has shown significant effectiveness in improving job safety, providing strong technical support for the safety protection of power tower climbing operations.

Journal of Physics Conference SeriesVol. 2876(1)
Power Grid Corporation (India) (IN)
Decent work and economic growth
Openalex Percentile: Top 83%
Power Systems and Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on fall prevention and safety protection for electric power tower climbing operations based on the longhorn whisker algorithm — Peng Zhan, Shuang Yu, et al. · Journal of Physics Conference Series (2024) | TGRS Research Map | TGRS