Automatically Controlling The Quality Of Steel Sheet Production Through Classification Of Surface Defects On Steel Sheet Using CNN
DOI:
https://doi.org/10.26593/jrsi.v12i2.5862.165-172Keywords:
classification, convolutional neural network, quality control, steel sheet, surface defectsAbstract
Quality control of steel sheet production using human visual perception frequently results in errors and takes longer. The implementation of deep learning to control the quality of steel sheet production can be done with high accuracy and in real time. The classification of surface defects on steel sheet is critical for automatically controlling the quality of steel sheet production. Deep learning can quickly identify and eliminate the causes of defects in sheet steel production by classifying the type of defects. This study aims to control the quality of steel sheet production automatically by classifying the types of surface defects using the Convolutional Neural Network (CNN). The CNN model used in this study is CNN with transfer learning from 5 pre-trained models Resnet50, VGG-16, VGG-19, Inception V3, and Xception. There are 6 types of surface defects, namely crazing, inclusion, pitted, patches, rolled, and scratch. The research begins with taking image data on steel sheet that has surface defects. The number of images used in this study is 1.800 images consisting of 1.152 training data, 288 validation data, and 360 testing data. Preprocessing is carried out, namely normalization, augmentation, and one-hot encoding. After preprocessing, training and validation was carried out using transfer learning from 5 pre-trained models. The training result model is used at the testing stage. The results of the training and validation show that Xception has the best performance because of the highest training and validation accuracy values, the lowest training and validation losses, and the lowest validation and loss GAPs. The test results show that transfer learning from the pre-trained Xception model has the best performance with an accuracy of 98%.
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