一个改进的基于YOLOv7的模型,用于实时计数器读取PConv和注意力机制
Xiancheng Peng1, Yangzhuo Chen2, Xiaowen Cai2
1Artificial Intelligence, Xiangtan University, Xiangtan 411100, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了一种高效的深度学习模型,用于准确的电网计数读数,改进了复杂环境的现有方法. 增强的YOLOv7模型实现了高精度和速度,适合边缘计算.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 电网电表读数面临复杂的表盘和传统检查准确性的挑战.
- 现有的深度学习识别系统是计算密集型的,阻碍了实时边缘设备的性能.
研究的目的:
- 开发一个高效和准确的深度学习模型,用于识别电网计量器.
- 为解决边缘计算应用程序当前模型的计算局限性.
主要方法:
- 提出了基于YOLOv7的改进的计量器识别模型.
- 部分卷积 (PConv) 被集成到YOLOv7中,以减少网络参数和操作.
- 动态头部 (DyHead) 模块被用来增强注意力机制,以更好地检测条纹物体.
主要成果:
- 改进的模型实现了97.87%的平均精度 (mAP50val) 和62.4%的mAP50:90val.
- 该模型只有537万个参数,推断速度为每秒108 (FPS).
- 检测精度达到±0.1度的电网表.
结论:
- 拟议的基于YOLOv7的模型提供了一种轻量级和高效的解决方案,用于准确识别电网计量器.
- 集成PConv和DyHead模块显著提高了边缘设备的性能和实时功能.
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