在基于YOLOv8s的电信基础设施周围检测异常活动
Enerst Edozie1, Aliyu Nuhu Shuaibu2, Ukagwu Kelechi John2
1Department of Electrical Engineering, Kampala International University, 20000, Ishaka, Uganda. edozie.enerst.11275@studwc.kiu.ac.ug.
Scientific reports
|November 5, 2025
概括
这项研究表明,YOLOv8s精确检测光纤电缆异常,如登风险. 定制数据集和优化模型显著提高了基础设施监控的检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 电信工程 电信工程 电信工程
- 人工智能的人工智能
背景情况:
- 光纤电缆基础设施对于现代通信至关重要.
- 爬山活动和环境损害等异常对电缆完整性构成风险.
- 现有的检测方法缺乏对各种异常类型的全面覆盖.
研究的目的:
- 评估YOLOv8s在光纤电缆中检测异常的有效性.
- 开发和验证一个定制的数据集,用于训练检测模型.
- 优化YOLOv8s模型,在现实场景中提高准确性和稳定性.
主要方法:
- 部署YOLOv8s (你只看一次版本8小) 对象检测模型.
- 创建一个自定义数据集,包含多种登和环境异常注释.
- 应用数据增强技术来改善模型概括.
- 在多个时代中对YOLOv8s模型进行培训和代测试.
主要成果:
- 最优化的YOLOv8s模型在100个时代后实现了97.3%的平均平均精度 (mAP@50).
- 该模型与原始YOLOv8s相比表现出更高的性能,精度更高 (96.9%) 和回忆率更高 (86.6%).
- 在检测准确度,可靠性和稳定性方面观察到显著的改进.
结论:
- YOLOv8s是用于检测光纤电缆异常的高精度和高效工具.
- 模型骨干优化和丰富,平衡的数据集是性能的关键.
- 这些发现支持实时部署YOLOv8s用于关键基础设施的操作现场监控.
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