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For the problem that current target detection methods generally require a high-power consumption GPU computing platform and are easily affected by lighting conditions, this paper proposed 2 infrared pedestrian detection methods in front of vehicles based on embedded platform: the trained YOLOv4-tiny model was optimized using NVIDIA’s open source inference acceleration library TensorRT and deployed on the embedded platform; the YOLOv4-tiny model was used as the basic architecture of the algorithm, which was combined with the visual attention mechanism and the spatial pyramid pooling idea, and a YOLO layer was added at the same time, a YOLOv4-tiny+3L+SPP+CBAM network model was proposed. The 2 methods were trained and tested on the FLIR dataset, and tested on the Jetson TX2 embedded platform. The test results show that: compared with the original network YOLOv4-tiny, the average accuracy of the first method is reduced by 0.54%, and the inference speed is increased by 86.43% (frame rate up to 26.1 frame/s), the average accuracy of the second method is improved by 16.21%, and the inference speed is reduced by 22.86% (frame rate up to 10.8 frame/s). Both methods can take into account the accuracy and real-time performance, and meet the needs of infrared pedestrian detection in front of vehicle.
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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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