| 陶 金,林文伟,曾 亮,张建寰,赵紫阳,徐周毅,张陈涛.基于 YOLOv4-tiny 和 Hourglass 的
指针式仪表读数识别[J].电子测量与仪器学报,2023,37(5):1-10 |
| 基于 YOLOv4-tiny 和 Hourglass 的
指针式仪表读数识别 |
| Pointer meter reading recognition based on YOLOv4-tiny and Hourglass |
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| DOI： |
| 中文关键词: 深度学习 指针式仪表检测 Hourglass 网络 YOLOv4-tiny |
| 英文关键词:deep learning pointer meter detection Hourglass network YOLOv4-tiny |
| 基金项目:国家重点研发计划(2018YFB1305703 )、福建省自然科学基金(2022J05107)、泉州市丰泽区科技计划项目(2022FZ01)资助 |
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| 中文摘要: |
| 为了降低电力巡检机器人识别变电站指针式仪表的误检率,提高仪表读数识别的精度,设计了一种基于深度学习的指
针式仪表检测方法。 通过在 YOLOv4-tiny 网络结构基础上添加残差模块来提高模型的鲁棒性,并对 Hourglass 网络结构改进,达
到精准识别指针式仪表读数的目的。 为了验证所提出方法的有效性,以变电站现场仪表图像数据对方法进行测试,并将检测结
果与其他方法进行对比。 实验结果表明,仪表定位漏检率仅 1. 25%,指针定位精度在 1. 125%以内,整体检测时间小于 0. 5 s。
相较于常用的 Hough 直线检测与 ORB 结合或基于 U-net 模型的方法,读数识别的平均误差分别降低了 70. 8%和 58. 8%,为变电
站指针式仪表的读数识别提供新的思路。 |
| 英文摘要: |
| In order to reduce the false detection rate of the electric inspection robot in identifying the pointer meter in the transformer
substation and improve the accuracy of meter reading identification, a pointer meter detection method based on deep learning is
proposed. By adding a residual module to the YOLOv4-tiny network to improve the robustness of the model and improvements to the
Hourglass network, precise identification of pointer meter readings is achieved. In order to verify the effectiveness of the proposed
method, the method is tested with the image data of the transformer substation and the test results are compared with other methods. The
experimental results show that the missing rate of the proposed approach is 1. 25%, the localization accuracy is less than 1. 125%, the
overall detection time was less than 0. 5 s. Compare with Hough line detection with ORB or U-NET, the average error of reading
recognition is reduced by 70. 8% and 58. 8%. The method provides new ideas for meter reading identification of transformer substations. |
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