Purposive Data Augmentation Strategy and Lightweight Classification Model for Small Sample Industrial Defect Dataset

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.