Application of detection and tracking for equipments in open-pit mines based on YOLOv8n+DeepSORT technology
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To address the inefficiencies of visual interpretation for mining equipment supervision in open-pit mines, this study proposes an enhanced YOLOv8n+DeepSORT framework. By leveraging high-point surveillance for image acquisition and optimizing both YOLOv8n for equipment identification and DeepSORT for real-time tracking, we overcome limitations in accuracy, cost, and real-time monitoring. Field validation at Jingkai Runding Mine, Pingxiang, Jiangxi, demonstrates the technology's efficacy in identifying and tracking mining equipment, featuring rapid algorithm convergence, low computational overhead, and near-precise target detection and tracking. This approach paves the way for algorithmic support to facilitate effective government regulation of open-pit mining operations.
Abstract
To address the inefficiencies of visual interpretation for mining equipment supervision in open-pit mines, this study proposes an enhanced YOLOv8n+DeepSORT framework. By leveraging high-point surveillance for image acquisition and optimizing both YOLOv8n for equipment identification and DeepSORT for real-time tracking, we overcome limitations in accuracy, cost, and real-time monitoring. Field validation at Jingkai Runding Mine, Pingxiang, Jiangxi, demonstrates the technology's efficacy in identifying and tracking mining equipment, featuring rapid algorithm convergence, low computational overhead, and near-precise target detection and tracking. This approach paves the way for algorithmic support to facilitate effective government regulation of open-pit mining operations.
참고문헌 (23)
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