Peng Xiancheng, Chen Yangzhuo, Cai Xiaowen, Liu Jun
Artificial Intelligence, Xiangtan University, Xiangtan 411100, China.
School of Automation and Electronic Information, Xiangtan University, Xiangtan 411100, China.
Sensors (Basel). 2024 May 31;24(11):3549. doi: 10.3390/s24113549.
With the increasing complexity of the grid meter dial, precise feature extraction is becoming more and more difficult. Many automatic recognition solutions have been proposed for grid meter readings. However, traditional inspection methods cannot guarantee detection accuracy in complex environments. So, deep-learning methods are combined with grid meter recognition. Existing recognition systems that utilize segmentation models exhibit very high computation. It is challenging to ensure high real-time performance in edge computing devices. Therefore, an improved meter recognition model based on YOLOv7 is proposed in this paper. Partial convolution (PConv) is introduced into YOLOv7 to create a lighter network. Different PConv introduction locations on the base module have been used in order to find the optimal approach for reducing the parameters and floating point of operations (FLOPs). Meanwhile, the dynamic head (DyHead) module is utilized to enhance the attention mechanism for the YOLOv7 model. It can improve the detection accuracy of striped objects. As a result, this paper achieves mAP50val of 97.87% and mAP50:90val of 62.4% with only 5.37 M parameters. The improved model's inference speed can reach 108 frames per second (FPS). It enables detection accuracy that can reach ±0.1 degrees in the grid meter.
随着电网电表表盘复杂度的增加，精确的特征提取变得越来越困难。针对电网电表读数已经提出了许多自动识别解决方案。然而，传统的检测方法在复杂环境中无法保证检测精度。因此，将深度学习方法与电网电表识别相结合。现有的利用分割模型的识别系统计算量非常大。在边缘计算设备中确保高实时性能具有挑战性。因此，本文提出了一种基于YOLOv7的改进电表识别模型。将部分卷积（PConv）引入YOLOv7以创建更轻量级的网络。在基础模块上使用了不同的PConv引入位置，以找到减少参数和浮点运算量（FLOPs）的最佳方法。同时，利用动态头部（DyHead）模块增强YOLOv7模型的注意力机制。它可以提高条纹物体的检测精度。结果，本文仅用537万个参数就实现了97.87%的mAP50val和62.4%的mAP50:90val。改进模型的推理速度可以达到每秒108帧（FPS）。它能够在电网电表中实现可达±0.1度的检测精度。
