  While deep neural networks have achieved remarkable performance, data
augmentation has emerged as a crucial strategy to mitigate overfitting and
enhance network performance. These techniques hold particular significance in
industrial manufacturing contexts. Recently, image mixing-based methods have
been introduced, exhibiting improved performance on public benchmark datasets.
However, their application to industrial tasks remains challenging. The
manufacturing environment generates massive amounts of unlabeled data on a
daily basis, with only a few instances of abnormal data occurrences. This leads
to severe data imbalance. Thus, creating well-balanced datasets is not
straightforward due to the high costs associated with labeling. Nonetheless,
this is a crucial step for enhancing productivity. For this reason, we
introduce ContextMix, a method tailored for industrial applications and
benchmark datasets. ContextMix generates novel data by resizing entire images
and integrating them into other images within the batch. This approach enables
our method to learn discriminative features based on varying sizes from resized
images and train informative secondary features for object recognition using
occluded images. With the minimal additional computation cost of image
resizing, ContextMix enhances performance compared to existing augmentation
techniques. We evaluate its effectiveness across classification, detection, and
segmentation tasks using various network architectures on public benchmark
datasets. Our proposed method demonstrates improved results across a range of
robustness tasks. Its efficacy in real industrial environments is particularly
noteworthy, as demonstrated using the passive component dataset.
