Recognition and Classification of Image Based Coal and Coal Gangue Using Deep Learning Techniques

Abstract

Observing coal features is the first step in learning about coal. Here, a coal gangue detection system was created that enables users to do a search even when they are unsure of the coal’s name by looking at specific coal features. Currently, machine vision is used to extract and analyze color, size, shape, and surface texture in coal classification. Even if the new extraction margin method can be applied generally, the margin of the extracted polygon’s form and the edge of the real image’s shape are still not satisfied. Finding the gangue in the coal is the project’s goal. Then, depending on the number of pixels representing the gangue colors, the entire gangue level in the coal data is determined and shown. This helps determine the coal’s quality. Even difficult-to-quantify qualities, like coal gangue, could be quantified in the future by academics who broaden their focus to include other features. The coal dataset is classified using an artificial neural network.