ENHANCING QUALITY CONTROL: CLASSIFICATION OF DEFECTIVE AND NON-DEFECTIVE PRODUCTS USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

The arrival of convolutional neural networks (CNNs) has enhanced the progress of computer visualisation from many fields. However, most of the CNNs are rely on GPUs (graphics processing units) that could need the large computations and it requires more cost to develop the setup. Therefore, most of the manufacturers haven’t used the CNNs to inspect the defective items in their field. The researcher has developed a compact CNN-based model that not only achieves high performance on tiny defect inspection but can be run on low-frequency CPUs (central processing units) in this paper. This experiments indicate CNNs can be compact and hardware-friendly for future applications in the automated surface inspect (ASI) in the selected manufacturing field.