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In the syringe scales defecting, it is difficult to collect samples and determine the type of defects. To overcome this issue, a method for detecting the quality of syringe scale with a small number of defective samples is proposed. This method leverages a large number of normal samples collected from an actual production line to train a deep scale segmentation model. The method constructs a Laplacian matrix based on pixel blocks of syringe scales to mine their correlation, and uses fuzzy c-means for unsupervised defect detection. Experimental results demonstrate that this syringe scale quality inspection method can detect all defective samples with 100% accuracy, effectively enhancing the production quality of medical syringes.