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The 3D point cloud object detection algorithm based on deep learning is prone to issues such as inability to maintain network performance and poor transferability when changing scenes or devices. To address this issue, this article proposes an Accurate, Flexible, and highly transferable two-stage 3D point cloud object detection algorithm (AF3D). In the first stage of the AF3D detection algorithm, a segmented fitting algorithm is used to remove the road surface from the collected laser point cloud, then DBSCAN algorithm is used to cluster non-ground point clouds and obtain several clustering clusters. In the second stage of the AF3D detection algorithm, a point cloud fully connected network PFC-Net is established, and features are extracted and classified. Through experiments, it has been proven that this algorithm can achieve good detection performance on public KITTI datasets, and the detection accuracy for cars, pedestrians, and cyclists on real vehicle datasets is 69.74%, 41.25%, and 54.33%, respectively, indicating good transferability.
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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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