Detection of Defects on Metal Surfaces Based on Deep Learning

Various types of defects can occur on metal surfaces during production due to various factors. Detecting these defects is of great importance for the quality and reliability of the product. Manual inspections are time-consuming and prone to errors, especially as production scales increase Although Deep Learning and Computer Vision techniques show promise, the large data sizes of datasets created for deep learning and the high training costs of deep learning models lead to negative effects in terms of storage and energy efficiency. We propose a new method to improve defect detection rates, reduce labor losses, decrease data sizes, and improve energy efficiency. For the experiments, we used the Northeastern University (NEU) surface defect database as a reference, and the MobileNetV2 architecture as the model. The deep learning model was trained separately using both the grayscale-converted NEU database and the database created with the proposed method, and the accuracies of the datasets were compared. Image preprocessing techniques such as morphological operations, Gaussian noise addition, Principal Component Analysis, and image thresholding with Otsu thresholding were used for defect detection. The model was trained using the newly created database, achieving a successful result with an average accuracy rate of 87% using the proposed algorithm.