Search Results (4)

Accurate real-time monitoring of underground conditions in coal mines is crucial for effective production management. However, limited computational resources and complex environmental conditions in mine shafts significantly impact the recognition and computational capabilities of detection models. This study utilizes a comprehensive dataset containing 117,887 images from five common underground mining tasks: mine personnel detection, large coal lump identification, conveyor chain monitoring, miner behavior recognition, and hydraulic support shield inspection. We propose the ESFENet backbone network, incorporating a Global Response Normalization (GRN) module to enhance feature capture stability while employing depthwise separable convolutions and HGRNBlock modules to reduce parameter volume and computational complexity. Building upon this foundation, we propose UCM-Net, a detection model based on the YOLO architecture. Furthermore, a self-supervised pre-training method is introduced to generate mine-specific pre-trained weights, providing the model with more semantic features. We propose utilizing the combined backbone and neck portions of the detection model as the encoder of an image-masking pre-training structure to strengthen feature acquisition and improve the performance of small models in self-supervised learning. Experimental results demonstrate that UCM-Net outperforms both baseline models and the state-of-the-art YOLOv12 model in terms of accuracy and parameter efficiency across the five mine datasets. The proposed architecture achieves 21.5% parameter reduction and 14.8% computational load decrease compared to baseline models while showing notable performance improvements of 1.3% (mAP_50:95) and 0.8% (mAP₅₀) in miner behavior recognition. The self-supervised pre-training framework effectively enhances training efficiency, enabling UCM-Net to attain an average mAP₅₀ of 94.4% across all five datasets. The research outcomes can provide key technical support for coal mine safety monitoring and offer valuable technological insights for the public safety sector. Full article

The motivation behind this research is the lack of an underground mining shaft data set in the literature in the form of open access. For this reason, our data set can be used for many research purposes such as shaft inspection, 3D measurements, simultaneous localization and mapping, artificial intelligence, etc. The data collection method incorporates rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, IMU Xsens MTi-30 and Faro Focus 3D. The ground truth data were acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D terrestrial laser scanner stations of a total 273,784,932 of 3D measurement points. This data set provides an end-user case study of realistic applications in mobile mapping technology. The goal of this research was to fill the gap in the underground mining data set domain. The result is the first open-access data set for an underground mining shaft (shaft depth −300 m). Full article

There are numerous traces of mining activity in the Miedzianka Mountain (Świętokrzyskie Mountains, Poland), because copper and silver ores have been mined in this region since at least the 13th century. The history of scientific research on the Miedzianka Mountain ore deposit spans almost 200 years. Almost 40 minerals have been found: ore minerals of Cu and Fe, and also secondary minerals, including carbonates, sulphates and even very rare arsenates, phosphates and vanadates. Three new minerals have been found, staszicite, lubeckite and miedziankite, but their chemical composition has not been precisely determined and therefore their names have not been approved by the International Mineralogical Association (IMA). The Miedzianka Mountain deposit is an important area on the map of educational activities. It is included in the “Świętokrzyskie Archaeological and Geological Trail” as a site of historical (mining and metallurgy) and natural (geological sciences) heritage. Despite the large potential, none of the underground workings (adits and shafts) are currently available to the public. Our research and exploration of the Teresa adit, which is one of the historical underground complexes of the Miedzianka Mountain, show that this adit displays a wide spectrum of topics in the field of mineralogy, geology and mining history. The Teresa adit, which is a 523 m system of underground corridors, contains 270 m of natural karst caves altered by mining works and is constituted of Upper Devonian limestones, locally cut by cherry shales. In several sites of the adit unique features can be observed, such as: (1) old mining works—galleries carved in the rock back in the 19th century; (2) interesting vein mineralization with secondary-colored copper carbonates and multi-colored calcite veins; (3) mineralization with azurite domination; and (4) karst phenomena (coatings, flowstone, dripstones and stalactites) in a cave part of the adit. The sites with unique features suggest that the Teresa adit is highly suitable to be presented to tourists. That is why we propose seven sites on the underground route that could be the basis for further projects to create a “geotouristic trail” in the Teresa adit. The proposal to make the Teresa adit available to tourists is in line with the tendency to protect the post-industrial landscape associated with former mining activities. Full article

Cessation of dewatering following underground mine closure typically results in groundwater rebound, because mine voids and surrounding strata undergo flooding up to the levels of the decant points, such as shafts and drifts. SIMPL (Simplified groundwater program In Mine workings using the Pipe equation and Lumped parameter model), a simplified lumped parameter model-based program for predicting groundwater levels in abandoned mines, is presented herein. The program comprises a simulation engine module, 3D visualization module, and graphical user interface, which aids data processing, analysis, and visualization of results. The 3D viewer facilitates effective visualization of the predicted groundwater level rebound phenomenon together with a topographic map, mine drift, goaf, and geological properties from borehole data. SIMPL is applied to data from the Dongwon coal mine and Dalsung copper mine in Korea, with strong similarities in simulated and observed results. By considering mine workings and interpond connections, SIMPL can thus be used to effectively analyze and visualize groundwater rebound. In addition, the predictions by SIMPL can be utilized to prevent the surrounding environment (water and soil) from being polluted by acid mine drainage. Full article