An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at https:/github.com/GuyooGu/MISC-Master.

To address the issue of low detection accuracy caused by low illumination and occlusion in underground coal mines, this study proposes an innovative miner detection method. A large dataset encompassing complex environments, such as low-light conditions, partial strong light interference, and occlusion, was constructed. The Efficient Channel Attention (ECA) mechanism was integrated into the YOLOv11 model to enhance the model’s ability to focus on key features, thereby significantly improving detection accuracy. Additionally, a new weighted Complete Intersection over Union (CIoU) and adaptive confidence loss function were proposed to enhance the model’s robustness in low-light and occlusion scenarios. Experimental results demonstrate that the proposed method outperforms various improved algorithms and state-of-the-art detection models in both detection performance and robustness, providing important technical support and reference for coal miner safety assurance and intelligent mine management.

The accurate measurement of vitrinite reflectance (especially for mean maximum vitrinite reflectance, MMVR) is an important issue in the fields of coal mining and processing. However, the application of MMVR has been somewhat hampered by the subjective and the time-consuming characteristic of manual measurements. Semi-automated methods that are oversimplified might affect the accuracy in measuring MMVR values. To address these concerns, we propose a novel MMVR measurement strategy based on machine learning (MMVRML). Considering the complex nature of coal, adaptive K-means clustering is firstly employed to automatically detect the number of clusters (i.e., maceral groups) in photomicrographs. Furthermore, comprehensive features along with a support vector machine are utilized to intelligently identify the regions with vitrinite. The largest region with vitrinite in each photomicrograph is gridded for further regression analysis. Evaluations on 78 photomicrographs show that the model based on random forest and 15 simplified grayscale features achieves the state-of-the-art root mean square error of 0.0424. In addition, to facilitate the usage of petrologists without strong expertise in the machine learning domain, we released the first non-commercial standalone software for estimating MMVR.