| Autor: |
Xinmin Li, Yingkun Wei, Jiahui Li, Wenwen Duan, Xiaoqiang Zhang, Yi Huang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 14, Iss 4, p 1664 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app14041664 |
| Popis: |
Object detection in unmanned aerial vehicle (UAV) images has become a popular research topic in recent years. However, UAV images are captured from high altitudes with a large proportion of small objects and dense object regions, posing a significant challenge to small object detection. To solve this issue, we propose an efficient YOLOv7-UAV algorithm in which a low-level prediction head (P2) is added to detect small objects from the shallow feature map, and a deep-level prediction head (P5) is removed to reduce the effect of excessive down-sampling. Furthermore, we modify the bidirectional feature pyramid network (BiFPN) structure with a weighted cross-level connection to enhance the fusion effectiveness of multi-scale feature maps in UAV images. To mitigate the mismatch between the prediction box and ground-truth box, the SCYLLA-IoU (SIoU) function is employed in the regression loss to accelerate the training convergence process. Moreover, the proposed YOLOv7-UAV algorithm has been quantified and compiled in the Vitis-AI development environment and validated in terms of power consumption and hardware resources on the FPGA platform. The experiments show that the resource consumption of YOLOv7-UAV is reduced by 28%, the mAP is improved by 3.9% compared to YOLOv7, and the FPGA implementation improves the energy efficiency by 12 times compared to the GPU. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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