In order to alleviate the severe dependence of the detection accuracy of pointer meter on the number of samples, and effectively improve the detection accuracy of pointer meter under the condition of few shot, a pointer meter detection method based on artificial-real sample metric learning is proposed. Firstly, the structure of pointer meter is statistically analyzed, and its significant features are extracted for modeling, so as to generate the required artificial benchmark samples to make up for the lack of pointer meter data in real scenario. Then, combined with the characteristics of metric learning, the Faster R-CNN is used as the baseline model to introduce the feature similarity metric module to reduce or eliminate the distribution difference between the artificial benchmark sample and the real sample in the low-dimensional feature vector space, and strengthen the feature extraction network to learn the significant features of the pointer meter. Experimental results show that, compared with the baseline model， AP75 of the pointer meter detection method based on artificial-real sample metric learning improves by 22.14%, which effectively improves the accuracy of pointer meter detection in the case of few shot.