LOSN: Lightweight ore sorting networks for edge device environment

Abstract

Vision-based intelligent ore sorting technology has been widely applied in current mining production, a trend further facilitated by the emergence of deep learning. However, most available implementations are still based on image classification, i.e., dividing the overall sorting task into two processes: classification and localization, without end-to-end integration. Meanwhile, harsh sorting scenarios make edge computing devices the primary candidate for model deployment, with more stringent limitations for model size, computational complexity, and inference speed. Therefore, this study proposes to integrate the operating processes to locate and classify the ores particles simultaneously. The lightweight structures, attention mechanisms, and multi-scale feature fusion strategies are applied in the architecture design to meet the deployment requirements of edge device environments and achieve a preferred accuracy–efficiency tradeoff, which leads to a new lightweight ore sorting networks called LOSN. In the case study, LOSN has the highest accuracy in multi-type and multi-class ore sorting tasks (78.87% and 80.64% in the gas coal and anthracite dataset, respectively) with fewer parameters (5.970M), lower GFLOPs (6.829G) and higher FPS (89.92), which is superior to commonly used high-performance object detection architectures (e.g., Yolo series, EfficientDet, Faster-RCNN, and CenterNet). Grad-CAM visualizations also demonstrate the feature extraction capability of LOSN.