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Outline
To address challenges in autonomous driving scenarios, such as uneven distribution of target scales, significant background interference, structural distortion of small objects, and high computational complexity, this paper proposes an improved algorithm, ELD-YOLO, based on YOLOv11n. The C3k2-EMSC module is introduced to enhance multi-scale feature extraction, capturing richer contextual information in complex traffic scenarios. The LSDECD module is designed to strengthen feature representation through shared convolutions and group normalization, reducing redundant responses. The DySample module is introduced to enhance small object and edge feature representation through dynamic sampling. Results demonstrate that on the KITTI and BDD100K datasets, the parameter quantity of ELD-YOLO is reduced by 16% compared with YOLOv11n, while achieving mAP@0.5s of 89.7% and 52.7%, respectively. This approach significantly improves detection accuracy and robustness while reducing model complexity.
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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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