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.

翻译：尽管深度神经网络已取得显著性能，数据增强仍是缓解过拟合、提升网络性能的关键策略。这些技术在工业制造场景中尤为重要。近年来，基于图像混合的方法虽在公开基准数据集上展现更优表现，但其在工业任务中的应用仍面临挑战。制造环境每天产生海量未标注数据，仅存在少量异常数据实例，导致严重的数据不平衡问题。由于标注成本高昂，构建均衡数据集并非易事，而这正是提升生产效率的关键环节。为此，我们提出专为工业应用与基准数据集设计的ContextMix方法：通过调整整幅图像尺寸并将其嵌入批次内其他图像来生成新数据。该方法能基于尺寸变换后的图像学习不同尺度的判别特征，并利用遮挡图像训练面向目标识别的辅助特征信息。在仅增加图像缩放最小计算成本的前提下，ContextMix相较现有增强技术提升了性能。我们在公开基准数据集上，采用多种网络架构评估其在分类、检测与分割任务中的有效性。所提方法在多项鲁棒性任务中均实现结果改善，尤其在无源元件数据集上展示的实际工业环境适应性值得关注。