With the advancement of deep learning algorithms,the recognition methods for pointer-based in-struments have evolved from traditional template matching algorithms to current deep learning-based ap-proaches.However,most commonly used deep learning methods for reading instruments rely on YOLO,which exhibit relatively poor robustness and struggle to meet the requirements of recognizing different types of instruments in a manufacturing workshop.This paper proposes an improved U-Net algorithm for segmen-ting the scales and pointers of instrument dials.A reading method for image segmentation is designed,and the key information of the instrument dial is extracted through text region detection and text recognition algo-rithms in OCR technology to achieve pointer-based instrument recognition.The proposed algorithm achieves a reading accuracy of up to 99％ for pointer-based instruments.Experimental results demonstrate excellent sta-bility and robustness of the algorithm,with higher accuracy compared to other deep learning methods.
关键词
深度学习/图像分割/U-Net/OCR/指针式仪表
Key words
deep learning/image segmentation/U-Net/OCR/pointer-based instruments
维普
万方数据