一种基于改进的YOLOv4模型的电力传输线路故障检测算法
Su Yan1, Lisha Gao2, Wendi Wang3
1Nanjing Suyi Industry Co., Ltd, Nanjing, 210000, Jiangsu Province, China.
Scientific reports
|February 29, 2024
概括
这项研究优化了YOLOv4网络以实时检测输电线路故障. 改进后的模型显著降低了参数,并提高了检测速度,确保了准确性.
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:
- 电力传输线路的实时故障检测对于电网稳定性至关重要.
- 现有的方法经常面临准确性和计算效率方面的挑战.
研究的目的:
- 优化YOLOv4网络,以改善输电线路故障检测.
- 为了减少模型参数,提高检测速度,同时保持准确性.
主要方法:
- 用更轻的EfficientNet网络取代了YOLOv4的骨干.
- 将集成的集成卷积模块集成到功能金字塔中.
- 使用了数据增强和DIoU损失函数.
主要成果:
- 模型参数减少了81% (至43.65万).
- 速率提高了85%,达到每秒24.
- 在损失函数优化和检测速度方面表现出卓越的性能.
结论:
- 优化的YOLOv4模型为电力传输线路故障检测提供了更有效,更准确的解决方案.
- 拟议的改进显著提高了计算效率和检测可靠性.
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