Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects and extend its application to new unseen target objects. We present a data enhancement technique to generate a large dataset from the small source dataset for building a classifier. We then integrate three different pre-trained models (Xception, ResNet101V2, and InceptionResNetV2) into the classifier network and compare their performance on source and target data. We use the classifier to detect the presence of imperfections on the unseen target data using pseudo-bounding boxes. Our results show that ResNet101V2 performs best on the source data with an accuracy of 95.72%. Xception performs best on the target data with an accuracy of 91.00% and also provides a more accurate prediction of the defects on the target images. Throughout the experiment, the results also indicate that the choice of a pre-trained model is not dependent on the depth of the network. Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data.

翻译：质量保证在智能制造业中至关重要，它能在成品出厂前识别产品是否存在缺陷。现代机器学习技术可被用于快速准确地检测这些瑕疵。为此，我们提出一种迁移学习方法——TransferD2，用于正确识别源对象数据集中的缺陷，并将其应用扩展到新的未知目标对象。我们提出一种数据增强技术，从较小的源数据集中生成大规模数据集以构建分类器。随后，我们将三种不同预训练模型（Xception、ResNet101V2和InceptionResNetV2）集成到分类器网络中，比较它们在源数据和目标数据上的性能。我们利用该分类器通过伪边界框检测未知目标数据中是否存在瑕疵。结果表明，ResNet101V2在源数据上表现最佳，准确率达到95.72%；Xception在目标数据上表现最优，准确率达91.00%，并能更精确地预测目标图像中的缺陷位置。整个实验还表明，预训练模型的选择与网络深度无关。我们所提出的方法可应用于训练数据不足的缺陷检测场景，并能扩展到识别新的未知数据中的瑕疵。