The Performance Analysis of Transfer Learning for Steel Defect Detection by Using Deep Learning

Abstract

Detecting steel surface defects is one of the challenging problems for industries worldwide that had been used in manufacturing quality management. Manual inspection of the steel surface detects is a time-consuming process. This work aims at developing deep learning models that can perform steel defect detection and evaluating the potential of transfer learning for this task. In this paper, four types of transfer learning methods: ResNet, VGG, MobileNet, and DenseNet are experimentally evaluated to develop models for steel surface defect detection. The models were developed for binary classification (defect and no-defect) using the SEVERSTAL dataset from that contains 12,568 images of the steel surface. Then, these models were also assessed for multiclass classification using NEU dataset with 1800 images. In this work, image pre-processing is included to improve the result of steel defects detection. The experimental results have shown that the model developed by using the MobileNet method have the highest detection rate with 80.41% for the SEVERSTAL dataset and 96.94% for the NEU dataset compare to ResNet, VGG, and DenseNet transfer learning.