Full
Outline
Existing key point detection algorithms tend to suffer from reduced detection precision, missed detections, or misaligned key points in scenarios with varying lighting conditions and dense crowds with overlapping figures. To address this issue, an improved LBW-YOLOv8n-Pose algorithm for multi-person pose estimation in complex environments is proposed based on YOLOv8n-Pose. By introducing the Large Separable Kernel Attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) layer of the feature extraction backbone network, the algorithm enhances the image feature representation and perception capabilities. A weighted Bidirectional Feature Pyramid Network (BiFPN) is incorporated in the neck network for reconstruction to improve the multi-scale feature fusion effect. Additionally, an improved Wise-IoU loss function is adopted to accelerate the model's convergence speed and enhance its robustness in complex scenarios. Experimental results show that the improved model achieves precision, recall, and average detection precision of 85.7%, 76.8%, and 81.7% respectively on the MS-COCO2017 human key point dataset, representing significant improvements over the original model. Moreover, it can more accurately and effectively detect key point information of multiple people in complex situations.
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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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