A Novel Strategy for Improving Robustness in Computer Vision
  Manufacturing Defect Detection
Ahmad Mohamad Mezher Andrew E. Marble
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.