Visual Recognition and Classification Scheme for Belt Conveyor Scenarios in Coal Mines
Abstract：Belt conveyors are important equipment in coal production operations. Due to their frequent high-load operation, they are prone to accidents such as misalignment, tearing, and mine fires. Based on a track-type inspection robot, by equipping a dual-light gimbal, real-time data on belt misalignment, foreign objects, material flow, tearing, and idler abnormalities are collected. By combining machine learning and deep learning methods, a visual recognition and classification method for belt conveyor scenarios is proposed, which improves the efficient intelligent perception of belt conveyors and the intelligence level of mining inspection robots, reducing the safety production losses caused by unplanned shutdowns in coal mines.
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Publication Date：2025-12-30
Online Publishing Date：2026-01-17(First online date of this platform, not the publication date of the document)
Pages：4( 86-89 )