Industrial defect detection plays a critical role in controlling product quality. Obtaining industrial defects with diverse and balanced classes in natural environments is often challenging. Most methods tend to uniformly augment all classes in small-sample datasets, which wastes computing resources and the classification performance is not always good. To achieve the purposive data augmentation, we propose a minority class imbalance rate (MiCIR) and an MiCIR-based data augmentation strategy that can determine the class and the number of samples to be augmented. In addition, to address the misclassification problem of classes with relatively large sample sizes, we introduce a lightweight classification model, ShcNet. We construct convolution-batchnorm-hard-swish (CBH) and convolution-batchnorm-hard-swish-convolutional block attention mechanism (CBHC) modules in ShcNet to improve classification performance. Experimental results demonstrate that our data augmentation strategy can significantly improve the classification results with generalizability across different datasets. The ShcNet outperforms the baseline models on classification accuracy while maintaining fewer parameters and model complexity.

中文翻译：

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小样本工业缺陷数据集的有目的数据增强策略和轻量级分类模型


工业缺陷检测在控制产品质量方面起着至关重要的作用。在自然环境中获得具有多样化和平衡类别的工业缺陷通常具有挑战性。大多数方法倾向于统一增强小样本数据集中的所有类，这会浪费计算资源，并且分类性能并不总是很好。为了实现有目的的数据增强，我们提出了少数类不平衡率（MiCIR）和基于 MiCIR 的数据增强策略，该策略可以确定要增强的类和样本数量。此外，为了解决样本量较大的类别的误分类问题，我们引入了轻量级分类模型ShcNet。我们在ShcNet中构建了卷积batchnorm-hard-swish（CBH）和卷积batchnorm-hard-swish-卷积块注意机制（CBHC）模块来提高分类性能。实验结果表明，我们的数据增强策略可以显着改善分类结果，并具有跨不同数据集的通用性。 ShcNet 在分类精度方面优于基线模型，同时保持较少的参数和模型复杂性。