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The defect detection and identification of plastic label is the key of industrial process control and quality control. In order to overcome the limitations of the plastic label defect detection methods, the single-stage target detection model YOLOv5 was used to detect and classify the defects in real time. In addition, in order to solve the problem of low model recognition accuracy due to insufficient number of sample defects, a Defect-GAN based generative adversarial networks is used for data augmentation and amplifying the data of small samples. By simulating the process of defect generation and defect image reconstruction, the method can efficiently synthesize a large number of defect samples with high fidelity and diversity. It is especially suitable for defect generation with irregular shape, random distribution and different size. The experimental results show that the accuracy of the detector can be significantly improved by using the amplified data set to train the detector and optimizing the network hyperparameters, and the mean Average Precision(mAP) of the detector can reach 99.5%. In addition, in order to simulate the application scenario of this method in actual production, a semi-automatic image acquisition machine platform was designed and customized for collecting printed labels on the surface of cylindrical samples, and a self-developed image processing and statistical analysis software was used for sample collection, image processing and statistical analysis. The method and platform can be easily extended and applied to other industrial quality control and defect detection systems.