Zou Le, Wang Kai, Wang Xiaofeng, Zhang Jie, Li Rui, Wu Zhize
School of Artificial Intelligence and Big Data, Hefei University, Heifei 230601, China.
Institute of Intelligent Machinery, Hefei Institute of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China.
Sensors (Basel). 2023 Jul 24;23(14):6644. doi: 10.3390/s23146644.
Meter reading is an important part of intelligent inspection, and the current meter reading method based on target detection has problems of low accuracy and large error. In order to improve the accuracy of automatic meter reading, this paper proposes an automatic reading method for pointer-type meters based on the YOLOv5-Meter Reading (YOLOv5-MR) model. Firstly, in order to improve the detection performance of small targets in YOLOv5 framework, a multi-scale target detection layer is added to the YOLOv5 framework, and a set of Anchors is designed based on the lightning rod dial data set; secondly, the loss function and up-sampling method are improved to enhance the model training convergence speed and obtain the optimal up-sampling parameters; Finally, a new external circle fitting method of the dial is proposed, and the dial reading is calculated by the center angle algorithm. The experimental results on the self-built dataset show that the Mean Average Precision (mAP) of the YOLOv5-MR target detection model reaches 79%, which is 3% better than the YOLOv5 model, and outperforms other advanced pointer-type meter reading models.
抄表是智能巡检的重要组成部分，而当前基于目标检测的抄表方法存在准确率低、误差大的问题。为提高自动抄表的准确率，本文提出一种基于YOLOv5-抄表（YOLOv5-MR）模型的指针式仪表自动读数方法。首先，为提高YOLOv5框架中小目标的检测性能，在YOLOv5框架中添加多尺度目标检测层，并基于避雷针表盘数据集设计一组锚框；其次，改进损失函数和上采样方法，以提高模型训练收敛速度并获得最优上采样参数；最后，提出一种新的表盘外圆拟合方法，并通过圆心角算法计算表盘读数。在自建数据集上的实验结果表明，YOLOv5-MR目标检测模型的平均精度均值（mAP）达到79%，比YOLOv5模型提高了3%，且优于其他先进的指针式仪表读数模型。
