ArticleScientific data2023
An open dataset for intelligent recognition and classification of abnormal condition in longwall mining.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- CGALS-YOLO: Vision-Based Sensing for Protective Equipment Wearing Compliance Detection in Underground Environments.Sensors (Basel, Switzerland) · 2026Article
- Vision-Based Detection of Large Coal Fragments in Fully Mechanized Mining Faces Using Adaptive Weighted Attention and Transfer Learning.Sensors (Basel, Switzerland) · 2026Article
- Integrated multimethod analysis of miners' safety behavior and risk interaction for practical applications.Scientific reports · 2025Article
- An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines.Scientific data · 2025Article
- Small target detection in coal mine underground based on improved RTDETR algorithm.Scientific reports · 2025Article
- Enhancing Object Detection in Underground Mines: UCM-Net and Self-Supervised Pre-Training.Sensors (Basel, Switzerland) · 2025Article
- A lightweight coal mine pedestrian detector for video surveillance systems with multi-level feature fusion and channel pruning.Scientific reports · 2025Article
- RCEAU-Net: Cascade Multi-Scale Convolution and Attention-Mechanism-Based Network for Laser Beam Target Image Segmentation with Complex Background in Coal Mine.Sensors (Basel, Switzerland) · 2024Article
- Research on the hydraulic support face guard mechanism and coupling characteristic of rib spalling in large mining heights.Scientific reports · 2024Article
- CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face.Sensors (Basel, Switzerland) · 2024Article
- An open dataset for intelligent recognition and classification of abnormal condition in longwall mining.Scientific data · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The underground coal mine production of the fully mechanized mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expect to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners' behaviour and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.