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The large number of parameters in deep learning models for driver distraction detection makes it difficult to deploy them on embedded devices. To address this issue, this paper proposes a lightweight distracted driving detection algorithm, YOLOv8n-SGC, based on YOLOv8n. First, a lightweight backbone network, ShuffleNetV2, is constructed, and Ghost convolution is introduced to reduce the number of model parameters and computational cost, achieving model lightweighting. Second, a Convolution and Attention Fusion Module (CAFM) is added after the backbone network to fuse global and local features and improve the algorithm’s detection accuracy. The results show that the improved algorithm model has a reduction in parameters and computational cost compared to the benchmark model, a 28.67% reduction in volume, a 41.79% reduction in inference time, and an mAP increase of 1.1 percentage points.
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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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