Fused attention mechanism-based ore sorting network

TL;DR

This paper introduces OreYOLO, a lightweight deep learning model with fused attention mechanisms and multi-scale feature fusion, significantly improving mineral ore detection accuracy and efficiency in complex environments.

Contribution

The study develops OreYOLO, a novel ore sorting network integrating attention and multi-scale fusion, achieving high accuracy with low computational cost for mineral resource identification.

Findings

Achieves 99.3% and 99.2% accuracy on gold and sulfuric iron ore detection.

Outperforms YOLO series, EfficientDet, Faster-RCNN, and CenterNet in detection tasks.

Maintains low parameters (3.458M) and GFLOPs (6.3) while delivering high performance.

Abstract

Deep learning has had a significant impact on the identification and classification of mineral resources, especially playing a key role in efficiently and accurately identifying different minerals, which is important for improving the efficiency and accuracy of mining. However, traditional ore sorting meth- ods often suffer from inefficiency and lack of accuracy, especially in complex mineral environments. To address these challenges, this study proposes a method called OreYOLO, which incorporates an attentional mechanism and a multi-scale feature fusion strategy, based on ore data from gold and sul- fide ores. By introducing the progressive feature pyramid structure into YOLOv5 and embedding the attention mechanism in the feature extraction module, the detection performance and accuracy of the model are greatly improved. In order to adapt to the diverse ore sorting scenarios and the deployment requirements of edge devices, the network structure is designed to be lightweight, which achieves a low number of parameters (3.458M) and computational complexity (6.3GFLOPs) while maintaining high accuracy (99.3% and 99.2%, respectively). In the experimental part, a target detection dataset containing 6000 images of gold and sulfuric iron ore is constructed for gold and sulfuric iron ore classification training, and several sets of comparison experiments are set up, including the YOLO series, EfficientDet, Faster-RCNN, and CenterNet, etc., and the experiments prove that OreYOLO outperforms the commonly used high-performance object detection of these architectures