Enhancing dragline operations supervision through computer vision: real time height measurement of dragline spoil piles dump using YOLO

Abstract

AbstractEffective monitoring of spoil pile heights resulting from dragline dumps is critical in mining space management both safely and productively, particularly, during active overburden (OB) removal. This study addresses this concern by replicating stable dump piles in alignment with the dragline balancing diagrams at a dynamic scale, optimizing in-pit volume use. Real-time tracking of dump pile heights ensures efficient dump disposal management, garnering attention in the mining industry. Monitoring dump height and shape during OB disposal near the dragline is vital. It is proposed to employ an experimental setup, consistently dumping specific volume samples from predefined heights at constant velocities. The technique uses You Only Look Once (YOLO) for dump pile height measurements. A benchmark dataset is created, encompassing various dragline dump configurations. YOLO achieves an F1-confidence score of 84.6% and a mean average precision (mAP) value of 99.49% in accurately recognizing dump profiles. To validate its reliability, output is compared with photogrammetry (SFM-MVS and NeRF). Employing 2D computer vision (AI) on simulated video data offers a fast, cost-effective, real-time solution for secure dump pile profile detection and height measurement, enhancing dragline mining efficiency with stable and safe dump heights as per design.

Keywords

• Computer vision
• YOLO
• dump pile detection
• open-cast mining
• autonomous tracking
• dragline dump monitoring