Investigating comparisons on the coal and gangue in various scenarios using multidimensional image features

Abstract

Online intelligent recognition of coal and gangue is an important aspect of coal mine intelligent development. Current research focuses more on improving recognition accuracy through optimizing image processing algorithms, but there is limited research on the characteristics of coal gangue materials themselves, particularly the comparative analysis of their appearance in images under different scenarios. Therefore, this paper proposes a multi-dimensional feature-based analysis method for coal-gangue images, which can be used to evaluate the characteristics and distinguishability of coal and gangue images. Firstly, the original feature space of coal and gangue images is constructed based on 55 global image feature operators. Then, the characteristics of coal and gangue images in both the original space and the two-dimensional space after dimensionality reduction are analyzed using the similarity between the left and right sides of the split violin plot and the distance of probability distributions. At the same time, different types of features are compared and analyzed in terms of their impact on classification performance when used as inputs with various machine learning classifiers. Furthermore, by leveraging the interpretability of tree models, the contribution of different feature types to coal gangue classification tasks is analyzed. The proposed method is applied to analyze a large number of raw coal images collected from three different conditions. The experimental results demonstrate that the distinguishability of coal gangue images varies under different scenarios, and the distance between the two distributions in the reduced space effectively measures this distinguishability. In coal gangue images, the contribution of color features and texture features to the classification task is related to the type of coal, with texture features playing a more significant role in classifying coking coal compared to thermal coal. However, overall, color features dominate the classification task. These findings are further validated in experiments using machine learning classifiers, which are crucial for designing more refined classification algorithms.