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作 者:邱慧敏 陈黎[1,2] QIU Huimin;CHEN Li(School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065;Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan University of Science and Technology,Wuhan 430065)
机构地区:[1]武汉科技大学计算机科学与技术学院,武汉430065 [2]武汉科技大学智能信息处理与实时工业系统湖北省重点实验室,武汉430065
出 处:《计算机与数字工程》2025年第3期857-864,共8页Computer & Digital Engineering
基 金:国家自然科学基金项目(编号:61773297)资助。
摘 要:现有指针式仪表读数识别方法主要依赖于神经网络模型精度且对图像质量要求较高,为此结合指针和表盘特点,提出基于指针方向和刻度定位的仪表读数识别方法。使用深度卷积神经网络分割出指针区域和表盘区域;指针区域确定指针旋转中心和指针方向点,得到指针的方向向量;指针旋转中心和表盘区域确定包含表盘的感兴趣区域,对其极坐标变换,灰度化、自适应阈值二值化后,用投影法统计刻度的像素值分布,确定刻度的位置,最后计算仪表读数。实验结果表明,相比于常规读数识别方法,指针的偏差角度显著降低45%,仪表读数的平均识别误差低至0.0717。The existing recognition methods of pointer meter reading network mainly depend on the accuracy of neural network model and have high requirements for image quality.Therefore,combined with the characteristics of pointer and dial,a method of reading recognition of meter based on pointer direction and scale positioning is proposed.This method uses deep convolution neural network to segment the pointer region and the dial region.The rotation center of pointer and the direction point of pointer are determined by the pointer region,then the direction vector of pointer is calculated.The rotation center of pointer and the dial region determine the region of interest including dial.After the polar coordinate transformation,graying and adaptive threshold binarization,the pixel value distribution of scale is counted by the projection method,and the position of scale is determined.Finally,the meter reading is calculated.The experimental results show that,compared with the conventional reading recognition method,the average angle error of pointer is significantly reduced by 45%,and the average error of reading of meter is as low as 0.0717.
关 键 词:指针式仪表 读数识别 图像分割 方向向量 刻度定位 极坐标变换
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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