基于GR-YOLOv8的传输线缺陷目标检测方法
Shuai Hao1, Kang Ren1,2, Jiahao Li1
1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了一种改进的YOLOv8模型,用于更快,更精确的输电线路故障检测. 新方法提高了准确性和速度,同时降低了电网基础设施的计算负载.
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:
- 传统的输电线路故障检测方法由于资源限制而面临速度和精度的限制.
- 现有的算法经常与特征冗余性和高计算需求作斗争.
研究的目的:
- 开发一种高效和准确的输电线路故障检测方法,使用增强的YOLOv8架构.
- 为了提高计算速度和精度,同时保持实时应用的轻量级模型.
主要方法:
- 拟议的方法将Rep (表示金字塔) 视觉转换器模块集成到YOLOv8 Neck中,以增强全球功能学习.
- 一个轻量级的GSConv (分组和分离的卷积) 模块被纳入脊柱和部以优化资源利用.
- 智能IOU (智能IOU) 损失函数用于改进边界盒回归 (BBR) 和减少梯度问题.
主要成果:
- 与原来的YOLOv8.8模型相比,增强的YOLOv8模型显示了0.058的提醒率和0.053的平均精度.
- 每秒的浮点运算减少了2.3,表明计算效率提高.
- 检测速度达到了114.9 FPS,显著增加了图片检测速度.
结论:
- 提出的基于YOLOv8的故障检测方法比传统和现有算法提供了更高的速度和精度.
- 整合Rep视觉变压器和GSConv模块有效平衡计算成本和检测性能.
- 这种方法为传输线路故障检测提供了一种轻量级但非常精确的解决方案.
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