To avoid the missing detection phenomenon caused by loss of local features due to the occlusion and truncation under complex traffic scenes, a LiDAR-based 3D object detection algorithm based on an improved PointPillars framework is proposed. First, the point cloud feature encoding network in PointPillars is enhanced to better preserve important structural information and the relationships within the point cloud data. Then, multi-scale point cloud features are extracted and aggregated through stacked sparse convolution modules and submanifold convolution modules. On the bird's-eye-view feature map that fuses these multi-scale features, 3D bounding boxes of objects are classified and regressed, improving the network's operational efficiency. Finally, experimental tests are conducted on the KITTI dataset. The results show that compared to the traditional PointPillars algorithm, the proposed 3D object detection method improves the Average Precision (AP) of 3D detection for challenging vehicle samples by 6.02 percentage points, and increases the AP for simple pedestrian and cyclist samples in the bird's-eye view by 3.35 and 3.18 percentage points respectively, effectively enhancing 3D object detection accuracy.
| 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 |