基于YOLOv10n-knot的电力安全带挂点检测算法

郑艳梅; 张欣; 芦碧波; 苏浩

doi:10.13637/j.issn.1009-6094.2025.085810.13637/j.issn.1009-6094.2025.0858

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基于YOLOv10n-knot的电力安全带挂点检测算法

安全评价安全工程 | 更新时间：2026-04-17

- 基于YOLOv10n-knot的电力安全带挂点检测算法
- 基于YOLOv10n-knot的电力安全带挂点检测算法
- 安全与环境学报 2026年26卷第4期页码：1316-1327
- 作者机构：
  
  河南理工大学计算机科学与技术学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目(42272178);2024年度河南省高等学校重点科研项目(24B520013)
- DOI：10.13637/j.issn.1009-6094.2025.085810.13637/j.issn.1009-6094.2025.0858
  中图分类号： TP391.41;TM08
- 纸质出版：2026
- 稿件说明：
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[1]郑艳梅,张欣,芦碧波,等.基于YOLOv10n-knot的电力安全带挂点检测算法[J].安全与环境学报,2026,26(04):1316-1327.
[1]郑艳梅,张欣,芦碧波,等.基于YOLOv10n-knot的电力安全带挂点检测算法[J].安全与环境学报,2026,26(04):1316-1327. DOI： 10.13637/j.issn.1009-6094.2025.085810.13637/j.issn.1009-6094.2025.0858.

DOI：10.13637/j.issn.1009-6094.2025.0858.

摘要

针对电力安全工作规程中高空作业安全带“高挂低用”的强制安全要求，提出了一种安全带挂点检测模型YOLOv10n-knot。首先，针对挂点目标尺寸小、特征易被主干网络忽略的问题，在主干网络中引入大核可分离卷积注意力机制，以增强安全带挂点的特征提取能力；其次，为提高对挂点的识别能力，在头部网络增加小目标检测层；最后，为增强对图像的特征提取能力，在颈部模块集成动态语义感知上采样算子，以提升模型对复杂场景的适应能力。试验表明，改进模型挂点检测的均值平均精度达98.5%

参数量减少至2.60×10

。与YOLOv8n、RT-DETR等模型相比，本模型在准确率、召回率及均值平均精度指标上均有显著提升。

Abstract

To comply with the mandatory safety requirements of the “high-position and low-use” standard in high-altitude electric power operations

this paper presents an enhanced high-precision safety harness anchor point detection model named YOLOv10n-knot. This model assesses compliance by detecting the relative spatial position between the safety harness and its anchor point. To tackle the challenge of detecting small anchor point targets that may be overlooked by the backbone network

we introduce a large-kernel separable convolution attention mechanism into the backbone architecture. This module utilizes sequential 1D convolutions in both horizontal and vertical directions

significantly improving the extraction of fine-grained local features. Secondly

a P2 small-object detection head with a resolution of 160×160 is added to the

model's architecture. This addition enhances the fusion of shallow high-resolution features with deep semantic information

thereby reducing the loss of critical features that can occur due to excessive downsampling. Thirdly

the model incorporates a dynamic semantic-aware upsampling operator in the neck region. By employing a content-aware mechanism to generate adaptive convolution kernels

this module enhances the quality of cross-scale feature fusion

thereby improving the model's robustness and localization precision in complex backgrounds. Experimental results demonstrate that the YOLOv10n-knot model excels in the anchor point detection task

achieving an accuracy of 97.4%

a recall of 97.3%

an mAP@50 of 98.5%

and an mAP@50:95 of 77.9%

all with a lightweight parameter size of only 2.60×10

. Compared with mainstream models such as YOLOv8n and RT-DETR

the proposed model shows improvements of 3.2 percentage points and 3.8 percentage points in accuracy

8.8 percentage points and 4.3 percentage points in recall

and 14.8 percentage points and 13.2 percentage points in mAP@50:95

respectively. Additionally

inference speed and memory consumption were evaluated across various hardware platforms. The results indicate that YOLOv10n-knot exhibits fast inference times and low memory usage

making it suitable for real-time deployment. In summary

YOLOv10n-knot achieves high detection accuracy while maintaining a lightweight structure. It demonstrates strong generalization and practical value

offering an effective solution for intelligent compliance detection of safety harness usage in high-altitude electric power operations.

关键词

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