使用SESYOLO在电力传输线路中检测异物
Pingting Duan1,2, Xuran Zhang3, Xiao Liang4,5,6
1Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Scientific reports
|March 3, 2026
概括
本研究介绍了一种人工智能驱动的算法,用于检测电线上的异物,显著提高了准确性和速度. 改进的模型在复杂环境中识别小,不规则的物体方面表现出色,提高了检查效率.
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:
- 外国物体附着在电力传输线路上是电气故障的常见原因.
- 现有的物体检测方法在精确的识别和电力线上的异物样本稀缺性方面扎.
研究的目的:
- 为了解决电力线上异物样本的稀缺问题.
- 为电力传输线路开发一个高精度,低延迟的外来物体检测算法.
主要方法:
- 利用人工智能生成的内容 (AIGC) 提供丰富,高质量的培训数据.
- 引入了空间和通道重建卷积 (SCConv) 以实现高效的特征学习.
- 实施高效修复的一般化FPN (高效RepGFPN) 以有效的信息交换.
- 开发了挤压和刺激探测器 (SE-Detect) 以更少的参数进行更丰富的特征提取.
- 采用WIoU损失函数和蒸方案以提高性能.
主要成果:
- 与YOLOv8.8相比,实现了mAP@.5的9%增加和召回率的9.1%改善.
- 特别用于鸟巢检测,证明了93.9%的mAP@.5.
- 该算法在混乱的视觉环境中有效检测到小型和不规则的外来物体.
结论:
- 开发的算法显著提高了电力传输线路上的异物检测.
- 结合AIGC,SCConv,高效RepGFPN和SE-Detect,可以提高精度并降低延迟.
- 该模型在具有小目标和复杂背景的空中检查场景中表现出特别高的有效性.
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