Evaluation of Data Augmentation on Surface Defect Detection

In this paper, we have investigated and benchmarked the augmentation approach of image augmentation to increase or provide a different result in detection accuracy compared to the basic method without augmentation. We explored two different methods of pixel-wise operations: pixel domain manipulation and spatial domain transformation to analyze the effect of increasing data for typical defect detection problems. We used two object detection models Faster R-CNN and Cascade R-CNN on top of ResNet-50 as our baseline models. To gain accuracy, we found that the effectiveness of data augmentation for defect detection is influenced by network complexity and the surface defect properties.