This study proposes a multi-range instrument recognition method based on YOLOv7+U2-Net to address the difficulties in locating instruments and low inference accuracy in the detection and recognition process of pointer instruments in complex environments. In order to improve the input image quality of the U2-Net model, a YOLOv7 detector with high inference accuracy and speed is selected. The detected and cropped images are used as the input image dataset of the model. At the same time, rotation correction is applied to the input image, making the model suitable for multi-angle instrument recognition. In response to issues such as poor accuracy and slow inference speed of instrument readings, the ordinary convolution of RSU4-RSU7 in the U2-Net decoding stage has been replaced with deep separable convolution. On this basis, an Attention mechanism has been introduced to accelerate the overall inference speed and accuracy. In addition, in order to improve the universal applicability of this method, a recognition accuracy discrimination method within multiple threshold ranges is proposed to adapt to various application scenarios. Through comparative experiments, it has been shown that when evaluated on the collected dataset, compared with template matching, SegNet, PSPNet, Deeplabv3+, and U-Net methods, the proposed method achieves a recognition accuracy of 96.5% and performs well in multiple threshold ranges.