Quality control using U-Net: detecting defects in leather

Abstract

Recently, there has been a significant amount of attention towards computer vision algorithms, particularly those that focus on semantic segmentation applications. This is due to the availability of big data to train models, as well as the computational ability of these algorithms. Among the various computer vision applications, the U-Net has gained popularity due to its reliable accuracy, simplicity in construction, and ease of application.Despite the advantages of this network structure, there are still some unclear aspects within the U-Net that have not been significantly covered in literature - to the best of our knowledge -. This study seeks to clarify and explain these ambiguous points and construct different architectures to demonstrate their pros and cons. Finally, a series of experiments were carried out on natural leather samples from MVTec AD to confirm our findings. The outcomes highlight our discoveries and provide a framework for determining the fine-tuning parameters of U-Net.