Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks like classification and object detection. Manufacturing data can pose a challenge for deep learning because data is highly repetitive and there are few images of defects or deviations to learn from. Deep learning models trained with such data can be fragile and sensitive to context, and can under-detect new defects not found in the training data. In this work, we explore training defect detection models to learn specific defects out of context, so that they are more likely to be detected in new situations. We demonstrate how models trained on diverse images containing a common defect type can pick defects out in new circumstances. Such generic models could be more robust to new defects not found data collected for training, and can reduce data collection impediments to implementing visual inspection on production lines. Additionally, we demonstrate that object detection models trained to predict a label and bounding box outperform classifiers that predict a label only on held out test data typical of manufacturing inspection tasks. Finally, we studied the factors that affect generalization in order to train models that work under a wider range of conditions.

翻译：高性能制造中的视觉质量检测可通过自动化获益，既能降低成本又可提高严谨性。深度学习技术是当前通用计算机视觉任务（如分类与目标检测）的主流方法。然而制造数据对深度学习构成挑战，因其具有高度重复性，且可供学习的缺陷或偏差图像极少。基于此类数据训练的深度学习模型可能脆弱且对上下文敏感，难以检测训练数据中未出现的新型缺陷。本研究探索训练缺陷检测模型以脱离上下文学习特定缺陷，从而提高其在新场景中的检测能力。我们证明了通过包含常见缺陷类型的多样化图像训练的模型，可在新环境下识别此类缺陷。这种通用模型对训练数据中未出现的新型缺陷具有更强的鲁棒性，并能减少在生产线上实施视觉检测的数据采集障碍。此外，研究还显示，同时预测标签与边界框的目标检测模型，在典型的制造检测任务保留测试数据上，优于仅预测标签的分类器。最后，我们探究了影响泛化能力的因素，以训练在更广泛条件下工作的模型。