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
针对现有仪表读数方法易受光照不均等因素影响,而导致读数误差大的问题,提出一种基于深度学习的全自动指针式仪表读数方法。首先,引入YOLOv7网络提取表盘区域;其次,采用文中提出的VCA-UNet(VGG16Net,improved skip connections and ASPP...针对现有仪表读数方法易受光照不均等因素影响,而导致读数误差大的问题,提出一种基于深度学习的全自动指针式仪表读数方法。首先,引入YOLOv7网络提取表盘区域;其次,采用文中提出的VCA-UNet(VGG16Net,improved skip connections and ASPP based U-Net)网络用于分割刻度线和指针;最后,引入PP-OCRv3网络自动获取仪表量程,并利用角度法确定仪表示数。实验结果表明:VCA-UNet网络的MIoU和MPA值较U-Net网络分别提升18.48%和9.36%,且普遍高于其他经典分割网络,仪表读数的平均相对误差为0.614%,且泛化实验的读数绝对误差相对较小,验证了读数方法的准确性和泛化性。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。
文摘针对现有仪表读数方法易受光照不均等因素影响,而导致读数误差大的问题,提出一种基于深度学习的全自动指针式仪表读数方法。首先,引入YOLOv7网络提取表盘区域;其次,采用文中提出的VCA-UNet(VGG16Net,improved skip connections and ASPP based U-Net)网络用于分割刻度线和指针;最后,引入PP-OCRv3网络自动获取仪表量程,并利用角度法确定仪表示数。实验结果表明:VCA-UNet网络的MIoU和MPA值较U-Net网络分别提升18.48%和9.36%,且普遍高于其他经典分割网络,仪表读数的平均相对误差为0.614%,且泛化实验的读数绝对误差相对较小,验证了读数方法的准确性和泛化性。
