Deep Learning in Undeground Mines - a Review

Through the newest advancements in the area of artificial intelligence, the popularity of deep learning has increased in almost every conceivable field. Underground mines are no exception to this trend, and although there is a noticeable delay, new technologies are also being implemented there. In t...

Full description

Saved in:
Bibliographic Details
Main Authors: Skoczylas, Artur, Gryncewicz, Wieslawa, Rosa, Agnieszka, Nadolny, Michal
Format: Conference Proceeding
Language:English
Subjects:
artificial intelligence
Deep learning
industry applications
Laser radar
Location awareness
Monitoring
Object recognition
Predictive maintenance
review
Reviews
Rocks
Training
underground mines
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Through the newest advancements in the area of artificial intelligence, the popularity of deep learning has increased in almost every conceivable field. Underground mines are no exception to this trend, and although there is a noticeable delay, new technologies are also being implemented there. In this paper, we present a review of deep learning applications in research concerning underground mines. The aim of this article is to outline the latest trends in this specific area; thus, only articles from recent years (2020-2024) were considered. Utilizing a Scopus query, initially 47 articles were identified, which were subsequently reduced to a final sample of 31. Through a comprehensive review of each article, the authors established five main lines of research in the field: predictive maintenance, efficiency assessment, localization and autonomous operation, object recognition, and early warning and safety. The article provides a broad overview of ongoing activities and future directions in these domains, along with a detailed catalog of individual research works and achievements.
ISSN:2770-5226
DOI:10.1109/ACIT62333.2024.10712588