An intelligent vision recognition method based on deep learning for pointer meters

Nowadays, pointer instruments remain the main state monitoring devices in the power industry, because they have strong mechanical stability to resist electromagnetic interferences compared with digital instruments. Although the object detection algorithms based on deep learning have widely been used in the field of instrument detection, the meter recognition process still relies on threshold segmentation to recognize object points and on Hough transform to extract the meter pointer. An intelligent vision recognition method based on YOLOv5 and U-2-Net network (YLU2-Net) is proposed to improve the accuracy and efficiency of meter recognition in a complex environment. Firstly, the pointer meter is located in the instrument images by using the YOLOv5 network as a region of interest (RoI). Then, the instrument RoI is processed by means of perspective transformation and image resizing. Thirdly, an improved U-2-Net image segmentation method with the deep separable convolution and the focal loss function is devised to distinguish the pointers and scales from the background in the instrument RoI. Further, a dimension reduction reading method with the polar coordinate transformation is developed to calculate the meter reading accurately and efficiently. Finally, the ablation experiment is conducted to test the performance of each algorithm module in our method, and the competition experiment is completed to compare our method with other state-of-the-art ones. The experimental results verify the accuracy and efficiency of the YLU2-Net recognition method proposed.

Automated summary

An intelligent vision recognition method based on YOLOv5 and U-2-Net network (YLU2-Net) is proposed to improve the accuracy and efficiency of meter recognition in a complex environment. The method uses YOLOv5 network to locate the pointer meter in instrument images and then processes the instrument region of interest (RoI) for accurate reading calculation. Experimental results verify the accuracy and efficiency of the proposed YLU2-Net recognition method.