Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper pro...Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper proposes a recognition method based on YOLOv8 and DeepLabv3+. To improve the image input quality of the DeepLabv3+ model, the YOLOv8 detector is used to quickly locate the instrument region and crop it as the input image for recognition. To enhance the accuracy and speed of pointer recognition, the backbone network of DeepLabv3+ was replaced with Mo-bileNetv3, and the ECA+ module was designed to replace its SE module, reducing model parameters while improving recognition precision. The decoder’s fourfold-up sampling was replaced with two twofold-up samplings, and shallow feature maps were fused with encoder features of the corresponding size. The CBAM module was introduced to improve the segmentation accuracy of the pointer. Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear power plants. Results showed that this method achieved a recognition accuracy of 94.5% at a precision level of 2.5, with an average error of 1.522% and an average total processing time of 0.56 seconds, demonstrating strong performance.展开更多
针对指针式仪表人工抄表的工作效率低、过失误差频繁出现等问题,基于机器视觉的自动抄表方法逐渐得到关注,提出了一种基于多形状模板定位策略的方法。方法的核心是提出的一种基于形状的目标出现的大概率偏好(High Probability Preferenc...针对指针式仪表人工抄表的工作效率低、过失误差频繁出现等问题,基于机器视觉的自动抄表方法逐渐得到关注,提出了一种基于多形状模板定位策略的方法。方法的核心是提出的一种基于形状的目标出现的大概率偏好(High Probability Preference for Target,HPPT)的模板匹配法,通过判断匹配目标的存在概率,对匹配过程进行优化,在保证匹配准确性的同时提高匹配的速度。通过模板匹配定位出指针旋转中心、指针中心点和零点刻度中心点,然后根据三个特征点得到指针偏转角,最后计算表针表示数。由实验可知,该方法可以对指针式仪表完成读数识别且平均引用误差为0.32%,平均检测一张表盘图像的时间为0.13 ms,精度和速度均优于对比实验。展开更多
针对仪表的智能识别方法在电力系统的工程应用,利用图像处理的方法对指针式仪表的智能读数进行研究;首先,针对摄像头多视角、多距离的安装问题,利用定向二进制描述符ORB(oriented FAST and rotated BRIEF)算法求解仪表模板图像与待测图...针对仪表的智能识别方法在电力系统的工程应用,利用图像处理的方法对指针式仪表的智能读数进行研究;首先,针对摄像头多视角、多距离的安装问题,利用定向二进制描述符ORB(oriented FAST and rotated BRIEF)算法求解仪表模板图像与待测图像的透视变换矩阵,用于定位指针旋转区域;然后根据表盘灰度特征信息,提出基于圆周区域的累积直方图法(circle-based regional cumulative histogram,CRH)对指针进行定位,由指针偏转角度得到读数;实验结果表明,该方法对指针读数识别十分有效,达到了实用化要求,且具有实时性好和精度高的特点。展开更多
文摘Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper proposes a recognition method based on YOLOv8 and DeepLabv3+. To improve the image input quality of the DeepLabv3+ model, the YOLOv8 detector is used to quickly locate the instrument region and crop it as the input image for recognition. To enhance the accuracy and speed of pointer recognition, the backbone network of DeepLabv3+ was replaced with Mo-bileNetv3, and the ECA+ module was designed to replace its SE module, reducing model parameters while improving recognition precision. The decoder’s fourfold-up sampling was replaced with two twofold-up samplings, and shallow feature maps were fused with encoder features of the corresponding size. The CBAM module was introduced to improve the segmentation accuracy of the pointer. Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear power plants. Results showed that this method achieved a recognition accuracy of 94.5% at a precision level of 2.5, with an average error of 1.522% and an average total processing time of 0.56 seconds, demonstrating strong performance.
文摘针对指针式仪表人工抄表的工作效率低、过失误差频繁出现等问题,基于机器视觉的自动抄表方法逐渐得到关注,提出了一种基于多形状模板定位策略的方法。方法的核心是提出的一种基于形状的目标出现的大概率偏好(High Probability Preference for Target,HPPT)的模板匹配法,通过判断匹配目标的存在概率,对匹配过程进行优化,在保证匹配准确性的同时提高匹配的速度。通过模板匹配定位出指针旋转中心、指针中心点和零点刻度中心点,然后根据三个特征点得到指针偏转角,最后计算表针表示数。由实验可知,该方法可以对指针式仪表完成读数识别且平均引用误差为0.32%,平均检测一张表盘图像的时间为0.13 ms,精度和速度均优于对比实验。
文摘针对仪表的智能识别方法在电力系统的工程应用,利用图像处理的方法对指针式仪表的智能读数进行研究;首先,针对摄像头多视角、多距离的安装问题,利用定向二进制描述符ORB(oriented FAST and rotated BRIEF)算法求解仪表模板图像与待测图像的透视变换矩阵,用于定位指针旋转区域;然后根据表盘灰度特征信息,提出基于圆周区域的累积直方图法(circle-based regional cumulative histogram,CRH)对指针进行定位,由指针偏转角度得到读数;实验结果表明,该方法对指针读数识别十分有效,达到了实用化要求,且具有实时性好和精度高的特点。
