基于YOLOv8和改进UNet++变电站指针式仪表读数识别
Reading Recognition of Substation Pointer Instrument Based on YOLOv8 and Improved UNet++
李春蕾 1阮艺铭 2张小明 3王宏淼 1王明杰4
作者信息
- 1. 许昌市分布式能源教学平台重点实验室,许昌 461000;许昌职业技术学院机电与汽车工程学院,许昌 461000
- 2. 郑州大学电气与信息工程学院,郑州 450001
- 3. 许继集团有限公司,许昌 461000
- 4. 许昌职业技术学院机电与汽车工程学院,许昌 461000
- 折叠
摘要
针对变电站仪表背景复杂、多旋转角度图像导致读数识别准确率低的问题,提出一种基于YOLOv8 和改进UNet++的指针式仪表读数识别方法.采用YOLOv8 模型检测仪表区域,并利用透视变换进行旋转校正;采用极化自注意力模块改进的UNet++算法分割表盘图像提取刻度、指针区域;经过提取指针直线后,采用角度法计算仪表读数.实验结果表明:提出方法识别仪表读数的平均引用误差为 1.82%,具有较高的识别准确性,将其应用于变电站指针式仪表智能化巡检中具有一定的可行性.
Abstract
This study proposes a method for pointer instrument reading recognition based on YOLOv8 and an improved UNet++to solve the problem of low reading recognition accuracy caused by complex backgrounds and multiple rotational angles in images of substation meters.YOLOv8 is utilized to detect the instrument area,and perspective transformation is used for rotation correction.The improved UNet++,enhanced by a polarized self-attention module,is utilized to segment dial images to extract scales and pointer regions.After the pointer line is extracted,the instrument reading is computed using the angle method.Experimental results indicate that the proposed method achieves an average citation error of 1.82%in identifying instrument readings.The method has superior recognition accuracy and is feasible for application in the intelligent inspection of pointer instruments in substations.
关键词
仪表读数识别/YOLOv8/UNet++/极化注意力/图像分割Key words
instrument reading recognition/YOLOv8/UNet++/polarized attention/image segmentation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据