A Novel Approach to Automated Coal Petrography Using Deep Neural Networks Souptik

Abstract

This research work is industry sponsored and carried out in collaboration with Tata Steel, India. Its objective is to alleviate a bottleneck in the steel manufacturing pipeline by the application of automated coal petrography. The problem can be de ned as generating semantic segmentation of microscopic coal petrography images. We are presented with a heavily imbalanced and weakly labelled dataset having major intensity based interclass confusion. We have attempted to solve this challenging problem by adopting a deep learning ap- proach to do away with the painful feature engineering process that is often a necessity in classical machine learning. The segmentation task is approached as a pixel level multiclass classi cation problem. Our novel solution uses ve binary U-Net classi ers in accordance with the One-vs-All approach to multiclass classi cation. These binary classi ers are trained using loss functions having additional regularization terms that we have developed in order to handle the interclass confusion problem. These regu- larizers have succesfully resolved majority of this confusion. The result obtained by amalgating the output of the binary classi ers is termed as coarse-segmentation and it su ers from both unclassi ed and misclassi ed pixels. These errors are corrected us- ing a post processing module having four self-developed image processing algorithms and a ne-segmentation is obtained as the nal result. Our solution's performance is benchmarked against two previous approaches based on a Miminum Distance Clas- si er and a Random Forest Classi er. Our method creates superior segmentations that have greater visual appeal and are more accurate. All experimental results are included to support our claim. It was also observed that our results were nearest to those obtained from the current, non-automated standard procedure used in the industry at present.