Automated Detection of Steel Defects via Machine Learning based on Real-Time Semantic Segmentation

Abstract

To improve automation, increase efficiency, and maintain high quality in the production of steel, applying modern machine learning techniques to help detect steel defects has been the research focus in the steel industry, since an unprecedented revolution in image semantic segmentation has been witnessed in the past few years. In the traditional production process of steel materials, localizing and classifying surface defects manually on a steel sheet is inefficient and error-prone. Therefore, it's a key challenge to achieve automated detection of steel surface defects in image pixel level, leaving an urgent and critical issue to be addressed. In this paper, to accomplish this crucial task, we apply a series of machine learning algorithms of real-time semantic segmentation, utilizing neural networks with encoder-decoder architectures based on Unet and feature pyramid network (FPN). The image dataset of steel defects is provided by Severstal, the largest steel company in Russia, through a featured code competition in the Kaggle community. The results show that the ensemble algorithm of several neural networks with encoder-decoder architectures has a decent performance regarding both time cost and segmentation accuracy. Our machine learning algorithms achieve dice coefficients over 0.915 and 0.905 at a speed of over 1.5 images per second on the public test set and private test set on the Kaggle platform, respectively, which locates at the top 2% among all teams in the competition.