Improved Surface Defect Classification from a Simple Convolutional Neural Network by Image Preprocessing and Data Augmentation
摘要
Convolutional neural networks (CNNs) play an important role in an increasing number of image processing tasks. There is an obvious demand to improve their classification performance and efficiency. Current research in this area tends to focus on developing increasingly complex models and algorithms to achieve this end. However, research into computer vision techniques and data augmentation tends to be neglected. This paper demonstrates that even a very simple CNN model achieves high performance in surface defect classification on the NEU dataset thanks to image preprocessing and data augmentation. The initial F1-score of 0.9646 without image preprocessing increases to 0.9727 when preprocessing is carried out. The simple CNN then achieves an F1-score of 0.9854 after data augmentation.