Lane line detection is a key technology in the field of autonomous driving, and it currently faces many challenges. The sparsity of the lane line supervision signal, as well as factors such as occlusion and shadows in complex scenes, can affect detection accuracy and realtime performance. Based on this, this paper proposes a lane line detection model that integrates the CBAM attention mechanism and a line anchor feature aggregation module. The proposed algorithm achieves an accuracy of 96.19% and a comprehensive F1 score of 76.24% on the Tusimple and CULane datasets, respectively. Real vehicle tests show that the algorithm detects a frame rate of 67 fps, allowing for realtime detection in complex traffic scenarios and more effectively addressing the problem of lane line occlusion.
| 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 |