Li Xiang, Zhao Jun, Zeng Changchang, Yao Yong, Zhang Sen, Yang Suixian
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
National Institute of Measurement and Testing Technology, Chengdu 610056, China.
Sensors (Basel). 2025 Jan 3;25(1):244. doi: 10.3390/s25010244.
With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images. To address these challenges, we propose an end-to-end PMRR method based on a decoupled circle head detection algorithm (YOLOX-DC) and a Unet-like pure Transformer segmentation network (PM-SwinUnet). First, according to the characteristics of the pointer dial, the YOLOX-DC detection algorithm is designed based on the exceeding you only look once detector (YOLOX). The decoupled circle head of YOLOX-DC detects the pointer meter dial more accurately than the commonly used rectangular detection head. Second, the window multi-head attention of the PM-SwinUnet network enhances the feature extraction ability of pointer meter images and solves problems of missed scale detection and incomplete pointer segmentation. Additionally, the scale and pointer fitting module is introduced into the PM-SwinUnet to locate the accurate position of the scale and pointer. Finally, through the angle relationship between the pointer and the first two main scale lines, the pointer meter reading is accurately calculated by the improved angle method. Experimental results demonstrate the effectiveness and superiority of the proposed end-to-end method across three-pointer meter datasets. Furthermore, it provides a rapid and robust approach to the digital transformation of manufacturing systems.
随着制造业的数字化转型，制造过程中的数据监测和收集变得至关重要。指针式仪表读数识别（PMRR）是整个制造过程数据监测的关键要素。然而，由于仪表图像存在模糊、光照不均、倾斜和背景复杂等问题，现有的PMRR方法准确率较低且鲁棒性不足。为应对这些挑战，我们提出了一种基于解耦圆头检测算法（YOLOX-DC）和类Unet纯Transformer分割网络（PM-SwinUnet）的端到端PMRR方法。首先，根据指针表盘的特点，基于你只看一次检测器（YOLOX）设计了YOLOX-DC检测算法。YOLOX-DC的解耦圆头比常用的矩形检测头能更准确地检测指针式仪表表盘。其次，PM-SwinUnet网络的窗口多头注意力增强了指针式仪表图像的特征提取能力，解决了刻度检测遗漏和指针分割不完整的问题。此外，在PM-SwinUnet中引入了刻度和指针拟合模块，以定位刻度和指针的准确位置。最后，通过指针与前两条主刻度线之间的角度关系，采用改进的角度方法准确计算指针式仪表的读数。实验结果证明了所提出的端到端方法在三指针仪表数据集上的有效性和优越性。此外，它为制造系统的数字化转型提供了一种快速且鲁棒的方法。
