YOLO-SS-Large:一个轻量级和高性能模型用于子站的缺陷检测
Qian Wang1, Lixin Yang1, Bin Zhou2
1Qujiang Campus, School of Electrical Engineering, Xi'an University of Technolgy, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
一个新的YOLO-SS大型模型增强了活跃配电网中的变电站缺陷检测. 这种轻量级模型提高了速度和精度,这对于安全和稳定的电网运行至关重要.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 活跃的配电网正在越来越多地采用可再生能源.
- 这些电网中的电站面临着及时检测缺陷和缓慢检查响应的挑战.
- 这些问题对活跃配电网的安全和稳定运行构成风险.
研究的目的:
- 提出一个高性能,轻量级的模型用于变电站缺陷检测.
- 为了提高复杂的变电站环境中小物体缺陷的检测.
- 提高智能变电站检查的整体效率和准确性.
主要方法:
- 基于YOLOv5m的YOLO-SS大型模型的开发,结合FasterNet结构以提高轻量级性能.
- 引入了一种新的NWD-CIoU损失函数,将标准化瓦瑟斯坦距离 (NWD) 和完整的交叉点 (CIoU) 融合在一起,以提高小物体检测.
- 集成一个动态头部模块来统一规模意识,空间意识和任务意识的注意力机制.
主要成果:
- 与YOLOv5m相比,YOLO-SS大型模型实现了0.3%的平均精度改进.
- 在每秒 (FPS) 中显示了43.5%的提升,表明速度增加.
- 实现了41.0%的参数减少,突出了它的轻量化性质.
结论:
- YOLO-SS大型模型为变电站缺陷检测提供了显著增强的全面性能.
- 该模型有效地满足智能变电站检查的速度和精度要求.
- 这一进步对于促进主动配电网的信息化和智能化建设至关重要.
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