Yildiz, AhmetÇakar, TunaTunal, Mustafa Mert2023-03-062023-03-062022Tunali, M. M., Yildiz, A., & Cakar, T. (2022). Steel Surface Defect Classification Via Deep Learning. 2022 7th International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk55850.2022.99194709781670000000https://hdl.handle.net/20.500.11779/1916https://doi.org/10.1109/UBMK55850.2022.9919470Deep learning and image processing methods have taken place in many parts of our lives, as well as in the quality control stages of production lines. The aim of this study is to train and use a deep learning model to improve quality management using limited data and computing power. To achieve that, deep learning for quality control models were trained by classifying six different steel surface defect images in the NEU-DET dataset. Xception, ResNetV2 152, VGG19 and InceptionV3 architectures were used to train the model. High accuracy was obtained with both Xception and ResNetV2 152. © 2022 IEEE.eninfo:eu-repo/semantics/openAccessSteel surface defectResnet152 v2Deep learningXceptionNeu-detImage classificationQuality controlVgg19Inception v3Conference Object10.1109/UBMK55850.2022.99194702-s2.0-85141881108