Full
Outline
To address the problems that existing foreign object detection algorithms for underground mine conveyor belts mainly model cross-channel dependencies through channel dimensionality reduction, resulting in high computational overhead and limited detection accuracy, a foreign object detection method based on attention-scale fusion and cross-spatial learning, termed ES-YOLO, was proposed. Firstly, an Efficient Multi-Scale Attention (EMA) module was introduced into the backbone network. By reshaping channels into the batch dimension and grouping channel dimensions into multiple sub-features, spatial semantic features were evenly distributed across different feature groups. Subsequently, a Scale Sequence Feature Fusion (SSFF) module was introduced to improve the network's capability for multi-scale feature extraction. By horizontally stacking feature maps at different scales and applying 3D convolution to capture scale-sequence features, the module effectively integrated high-dimensional semantic information from deep feature maps with detailed information from shallow feature maps. Furthermore, a Dimension-Aware Selective Integration (DASI) module was introduced to adaptively assign weights to high-dimensional and low-dimensional feature maps, thereby improving the model's ability to detect small objects. Experimental results demonstrate that, compared with the baseline YOLOv8n model, the proposed method increases the mean average precision by 1.5 percentage points to 89.7%. The class-wise mean average precision for large ore blocks improve by 4.1 percentage points, while the model parameters are reduced by 18%. Compared with mainstream models, the proposed method has significant advantages in detection accuracy and lightweight, making it more suitable for real-time foreign object detection in underground conveyor belts in mines.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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