A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection
This addresses the challenge of implementing automated visual inspection in high-performance manufacturing by potentially reducing data collection needs and improving robustness, though it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of deep learning models being fragile and under-detecting new defects in manufacturing visual inspection due to repetitive data and few defect images, by training models to learn specific defects out of context, resulting in more robust detection in new situations and outperforming classifiers on held-out test data.
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.