  Visual steel surface defect detection is an essential step in steel sheet
manufacturing. Several machine learning-based automated visual inspection (AVI)
methods have been studied in recent years. However, most steel manufacturing
industries still use manual visual inspection due to training time and
inaccuracies involved with AVI methods. Automatic steel defect detection
methods could be useful in less expensive and faster quality control and
feedback. But preparing the annotated training data for segmentation and
classification could be a costly process. In this work, we propose to use the
Transfer Learning-based U-Net (TLU-Net) framework for steel surface defect
detection. We use a U-Net architecture as the base and explore two kinds of
encoders: ResNet and DenseNet. We compare these nets' performance using random
initialization and the pre-trained networks trained using the ImageNet data
set. The experiments are performed using Severstal data. The results
demonstrate that the transfer learning performs 5% (absolute) better than that
of the random initialization in defect classification. We found that the
transfer learning performs 26% (relative) better than that of the random
initialization in defect segmentation. We also found the gain of transfer
learning increases as the training data decreases, and the convergence rate
with transfer learning is better than that of the random initialization.
