  A current trend in industries such as semiconductors and foundry is to shift
their visual inspection processes to Automatic Visual Inspection (AVI) systems,
to reduce their costs, mistakes, and dependency on human experts. This paper
proposes a two-staged fault diagnosis framework for AVI systems. In the first
stage, a generation model is designed to synthesize new samples based on real
samples. The proposed augmentation algorithm extracts objects from the real
samples and blends them randomly, to generate new samples and enhance the
performance of the image processor. In the second stage, an improved deep
learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and
a Residual Network is proposed to perform object detection on the enhanced
dataset. The performance of the algorithm is validated and evaluated on two
multi-class datasets. The experimental results performed over a range of
imbalance severities demonstrate the superiority of the proposed framework
compared to other solutions.
