Defect detection of injection molding products on small datasets using transfer learning

Appearance defect detection of products is a demanding procedure in the manufacturing process. Existing appearance defect inspection mainly relies on manual visual inspection, which is neither efficient nor accurate enough to ensure the manufacturing quality. Thus, automatic defect inspection has become an urgent demand. A critical problem hindering extensive applications of automatic defect inspection is that there are only limited defective samples to develop classification algorithms, leading to inadequate accuracy or robustness to meet industrial requirements. This paper proposed a knowledge reuse strategy to train convolutional neural network (CNN) models to improve defect inspection accuracy and robustness. By introducing model-based transfer learning and data augmentation, the knowledge from other vision tasks is transferred to industrial defect inspection tasks, resulting in high accuracy with limited training samples. Experimental results on an injection molding product showed that the detection accuracy was improved to about 99% when only 200 images per category were available. In comparison, conventional CNN models and the support vector machine method could achieve an average accuracy of only about 88.70% and 86.90%, respectively. The proposed method was also robust enough in detecting complicated defects which had many diversified appearances. The visualization method also proved that the performance improvement of the proposed method was because the model accurately extracted the discriminative features of the defective regions in the input images. The proposed method is meaningful for automatic defect detection in the manufacturing process.