基于DCP-YOLOv8的输电线路的轻量级和高效的多种类型缺陷检测方法
Yong Wang1, Linghao Zhang2, Xingzhong Xiong1
1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了DCP-YOLOv8,这是一种用于高效准确检测电力线图像缺陷的AI模型. 它与轻质结构平衡了高精度,提高了实时检查能力.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 电气工程 电气工程
背景情况:
- 智能故障检测对电力线路检查至关重要,对电网可靠性至关重要.
- 现有的AI模型在多种类型的缺陷识别中面临着效率 (轻量级) 和准确性 (复杂) 之间的权衡.
- 对于实时应用而言,需要一个可以实现高精度,同时保持轻量结构的模型.
研究的目的:
- 建议使用人工智能图像识别的输电线路采用轻量级和高效的多类型缺陷检测方法.
- 为了增强缺陷特征提取和融合,在各种尺度上提高检测准确度.
- 为了平衡高检测精度与缩小模型大小和增加处理速度.
主要方法:
- 开发了一种基于DCP-YOLOv8的新方法,用于传输线路缺陷检测.
- 采用可变形卷积 (C2f_DCNv3) 来提高特征提取能力.
- 集成了重新参数化的交叉相特征融合结构 (RCSP) 和具有可变形卷积v3 (DCNv3-Dyhead) 的动态检测头,用于增强特征表达和上下文信息利用.
主要成果:
- 在20个真实输电线路缺陷的数据集上实现了72.2%的平均准确性 (mAP@0.5),比YOLOv8n基线增加4.3%.
- 将模型参数减少到280万,减少9.15%,同时保持高检测精度.
- 达到每秒103 (FPS) 的处理速度,满足实时检测需求.
结论:
- 拟议的DCP-YOLOv8方法有效地平衡了检测准确性和性能,用于在输电线路中识别多种类型的缺陷.
- 该模型显示出强大的定量概括性,并满足实时检测要求.
- 这种方法在人工智能驱动的电网输电线路检查方面取得了重大进展.
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