YOLOv7-CWFD用于实时检测输电线路上的螺栓缺陷
Lincong Peng1, Kerui Wang1, Hao Zhou1
1School of Information, Yunnan University, Kunming, 650504, China.
Scientific reports
|January 10, 2025
概括
本研究介绍了YOLOv7-CWFD,这是一个改进的实时模型,用于检测输电线路上的螺栓缺陷. 它实现了更高的精度和更小的模型尺寸,提高了电力系统的安全性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 确保电力系统的安全依赖于检测输电线路上的螺栓缺陷.
- 现有的检测方法缺乏足够的准确性和实时应用的最佳模型大小.
研究的目的:
- 为传输线路开发一个实时螺栓缺陷检测模型,以提高准确性和减少模型大小.
- 提高电气基础设施自动化检查的效率和有效性.
主要方法:
- 建议YOLOv7-CWFD,整合通道杂交多样路径聚合网络 (CSDPAN) 以减少复杂性.
- 采用加权的高效交叉与联盟 (EIoU) 和规范瓦斯斯坦距离 (NWD) 损失函数,以改进回归.
- 集成了快速里埃频道注意力机制 (FFCAM) 和DySample上采样操作员来保存详细的信息.
主要成果:
- 在TLBDD数据集上,模型参数大小减少10.30MB,平均平均精度 (mAP) 增加2.30%.
- 演示了每秒51.15 (FPS) 的检测速度.
- 在公开的CCTSDB数据集上验证了强大的概括能力.
结论:
- 拟议的YOLOv7-CWFD模型有效地解决了传输线螺栓缺陷检测现有方法的局限性.
- 新型组件的集成显著提高了检测准确性,模型效率和稳定性.
- 这些发现支持采用这种先进的模型来实时监控和维护电力系统.
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