LMD-YOLO:基于改进的YOLOv8的轻量级算法用于发电网绝缘体的多重缺陷检测
Weiyu Han1, Zixuan Cai1, Xin Li2
1School of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang, China.
PloS one
|February 21, 2025
概括
一个新的轻量级网络,LMD-YOLO,增强了电网绝缘体缺陷检测. 它提高了准确性,降低了模型的复杂性,为智能电网检查提供了有效的解决方案.
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:
- 绝缘体缺陷检测对于配电网络可靠性至关重要.
- 由于各种类型的缺陷,现有的方法面临准确性,复杂性和参数数量的挑战.
研究的目的:
- 开发一种轻量级,准确的绝缘体多缺陷检测网络.
- 解决智能电网检查当前模型的局限性.
主要方法:
- 基于YOLOv8的拟议LMD-YOLO网络,将SCConv模块纳入骨干.
- 集成的SimAM注意力机制,用于增强特征提取.
- 使用SIoU损失函数,以提高收率和精度.
- 创建了一个专门的绝缘体缺陷检测数据集.
主要成果:
- 与YOLOv8n相比,LMD-YOLO在绝缘体数据集上的平均准确度提高了2%.
- 模型参数减少了24.6%,显著降低了计算复杂性.
- 证明有效检测各种绝缘体缺陷,包括缺失,破损和脱落类型.
结论:
- 在智能电网检查中,LMD-YOLO为绝缘体缺陷检测提供了有效和高效的解决方案.
- 拟议的网络平衡了准确性和模型轻量级,超过了现有的基准.
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