[1]张　震,刘建昌,葛帅兵,等.基于改进YOLOv8的指针式仪表读数识别算法[J].郑州大学学报(工学版),2026,47(3):83-91.[doi:10.13705/j.issn.1671-6833.2025.06.008]
　ZHANG Zhen,LIU Jianchang,GE Shuaibing,et al.Pointer Meter Reading Recognition Algorithm Based on Improved YOLOv8[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):83-91.[doi:10.13705/j.issn.1671-6833.2025.06.008]
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基于改进YOLOv8的指针式仪表读数识别算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
83-91
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
Pointer Meter Reading Recognition Algorithm Based on Improved YOLOv8
文章编号:
1671-6833(2026)03-0083-09
作者:
张　震1,2, 刘建昌1, 葛帅兵1, 张俊杰3, 张　凯3
1.郑州大学 河南先进技术研究院,河南 郑州 450001;2.郑州大学 电气与信息工程学院,河南 郑州 450001;3.河南汇融油气技术有限公司,河南 郑州 450001
Author(s):
ZHANG Zhen1,2, LIU Jianchang1, GE Shuaibing1, ZHANG Junjie3, ZHANG Kai3
1.Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China; 2.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 3.Henan Huirong Oil & Gas Technology Co., Ltd., Zhengzhou 450001, China
关键词:
仪表读数识别;  轻量化YOLOv8;  SIFT倾斜校正;  关键点检测;  角度法
Keywords:
meter reading recognition;  lightweight YOLOv8;  SIFT tilt correction;  key point detection;  angle method
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2025.06.008
文献标志码:
A
摘要:
针对现有深度学习的仪表读数识别算法在边缘设备上存在资源消耗较大和传统图像处理算法在复杂场景下鲁棒性不足、误差累积以及难以实现端到端指针提取等问题,提出了一种轻量化改进的YOLOv8模型实现仪表检测,并采用YOLOv8-pose关键点模型提取仪表盘关键点,拟合指针与刻度线结合角度法计算仪表读数。首先,设计轻量级的RGELAN模块替代C2f模块,降低骨干和颈部网络复杂度;其次,将考虑多尺度特征贡献的CASCHead检测头替代解耦头,减少检测头参数量;最后,引入Shape-IoU优化回归损失函数,提升检测准确性。实验结果表明: 改进后的模型YOLOv8-RSS在P和mAP@50:95上分别为98.5%和90.6%,相较于原始YOLOv8的P和mAP@50:95仅损失了0.3%和0.4%,但参数量、计算量和模型大小分别减少了48.3%,44.4%和46%;在复杂场景下,仪表读数阶段算法的平均相对误差、平均引用误差、参数量和检测速度分别为1.425%,0.557%,3.08M和78帧/s,与其他算法相比,该算法降低了空间占用和读数误差,提升了检测速度。
Abstract:
To address the issues of existing deep learning-based meter reading algorithms on edge devices, such as high resource consumption, the lack of robustness, error accumulation, and difficulty in end-to-end pointer extraction in traditional image processing methods in complex scenarios, a lightweight improved YOLOv8 model was proposed for meter detection. Meanwhile, the YOLOv8-pose keypoint model was employed to extract keypoints from the meter dial, and the reading was calculated using an angle-based method by fitting the pointer and scale lines. Firstly, a lightweight RGELAN module was designed to replace the C2f module, reducing the complexity of the backbone and neck networks. Then, the CASC-Head detection head, which considered multi-scale feature contributions, replaced the decoupled head, reducing detection parameters. Finally, the Shape-IoU optimized regression loss was introduced to improve detection accuracy. Experimental results showed that the improved YOLOv8-RSS model achieved 98.5% precision and 90.6% mAP@50:95, with only 0.3% and 0.4% losses compared with the original YOLOv8, while reducing parameters, computation, and model size by 48.3%, 44.4%, and 46%, respectively. In complex scenarios, it achieved an average relative error of 1.425%, average absolute error of 0.557%, 3.08 MB parameters, and 78 frame per second. Compared with existing methods, the proposed algorithm reduced space consumption and reading errors, and improved detection speed.

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更新日期/Last Update: 2026-05-27