YOLOv5s-TC:基于YOLOv5s的绝缘器故障检测的改进智能模型
Yingying Yin1, Yunpeng Duan1, Xin Wang1
1College of Information Technology, Jilin Agricultural University, Changchun 130118, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究介绍了YOLOv5s-TC,这是一个改进的深度学习模型,用于使用无人机检测电网中的绝缘体故障. 它显著提高了检测精度和可靠性.
科学领域:
- 电气工程
- 计算机视觉
- 人工智能
背景情况:
- 绝缘体是电网中的关键组件,需要可靠的故障检测以确保安全和运行完整性.
- 传统的手工检查是劳动密集型的,容易出错,需要先进的自动化解决方案.
- 无人机技术与深度学习相结合,为高效的电网检查提供了有前途的方法.
研究的目的:
- 开发一个先进的智能绝缘体检测模型,以提高电网检查的准确性.
- 系统地改进YOLOv5s深度学习模型以提高故障检测能力.
- 根据现有最先进的方法验证拟议模型的性能.
主要方法:
- 该研究提出了基于YOLOv5s的增强绝缘体检测模型YOLOv5s-TC.
- 主要修改包括用瓶变压器取代C3模块,集成卷积块注意模块 (CBAM),并采用OSIoU损失函数.
- 对YOLOv5s,Faster R-CNN和SSD模型进行了比较实验.
主要成果:
- 与基线模型相比,YOLOv5s- TC显示出显著的性能改善.
- 与YOLOv5s相比,平均精度 (mAP) 提高了4. 4%,与Faster R-CNN相比提高了24. 5%,与SSD相比提高了13. 9%.
- 增强的模型显示出优异的目标定位和特征学习能力.
结论:
- 拟议的YOLOv5s-TC模型为实际电网检查应用提供了卓越的检测性能和可靠性.
- 结合瓶变压器,CBAM和OSIoU有效提高了深度学习模型的准确性.
- 这一进步有助于通过基于无人机的自动化检查来实现更安全,更有效的电网维护.
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