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一个基于注意力的双向特征融合算法用于绝缘体检测.

Binghao Gao1, Jinyu Guo1, Yongyue Wang1

  • 1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.

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概括
此摘要是机器生成的。

本研究介绍了MC-YOLO,这是一个改进的You Only Look Once (YOLO) 算法,用于检测电力线路中的绝缘体缺陷. 改进的模型在识别关键缺陷方面取得了更高的准确性,确保了输电可靠性和安全性.

关键词:
注意力机制注意力机制深度学习是一种深度学习.功能融合功能融合功能绝缘体检测检测 绝缘体检测 绝缘体检测多个尺度的特征是多个尺度的特征.

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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:

  • 电力线路绝缘器缺陷对电网可靠性,安全性和可持续性构成风险.
  • 现有的你只看一次 (YOLO) 算法与复杂的背景和小缺陷大小作斗争,导致检测不准确.
  • 准确的绝缘器缺陷检测对于有效的电力线路检查至关重要.

研究的目的:

  • 开发一个改进的基于You Only Look Once 5 (YOLOv5) 版本的算法,用于增强绝缘体缺陷检测.
  • 解决现有方法的局限性,特别是复杂场景中的低检测精度.
  • 通过先进的缺陷识别来提高电力传输系统的可靠性和安全性.

主要方法:

  • 提出了MC-YOLO算法,这是一个增强的YOLOv5模型,包含了新的模块.
  • 引入了多尺度注意力卷积融合 (MACF) 模块,以提取多尺度特征并使用注意力机制增强目标表示.
  • 开发了一种跨语境特征融合模块 (CCFM) 与空间通道双重注意模块 (SCDAM),用于有效地融合浅层和深层特征.

主要成果:

  • 在测试数据集中,MC-YOLO算法获得了67.4%的平均平均精度 (mAP@0.5).
  • 与原始YOLOv5模型相比,检测准确度有4.1%的改善.
  • 虽然稍微降低了每秒 (FPS),但该模型仍然保持了用于快速准确检测缺陷的实际性能.

结论:

  • MC-YOLO算法显著提高了电力线路中绝缘体缺陷检测的准确性.
  • 拟议的MACF和CCFM模块有效地增强特征提取和融合,以提高检测性能.
  • MC-YOLO提供了一种实用且准确的解决方案,以保持输电可靠性和安全性.