Transfer learning for automated optical inspection

One of the challenges in applying convolutional neural networks to automated optical inspection is the lack of sufficient training data. In this paper we show that transfer learning can be successfully applied using image data from an entirely different domain. Focusing on optical inspection of texture images, we transfer weights from a source network trained with arbitrary unrelated images from the ImageNet dataset. Inspection experiments using our method show that one epoch of fine-tuning is sufficient to achieve 99.95% classification accuracy, while conventional transfer learning without fine-tuning achieves only 78.76%. An in-depth analysis of the effects of fine-tuning reveals that after fine-tuning, most of the unnecessary features encoded in the weights of the source network are deactivated, while meaningful features of the target data are amplified to capture new variations in the target domain.