YOLOv7-UAV: Improved YOLOv7 Algorithm for Small Object Detection in UAV Image Scenarios

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 have dense object regions, posing a significant challenge to small object detection. To slove this issue, we propose an efficient YOLOv7-UAV algorithm in which a low-level prediction head P2 which is added to detect the small targets from the shallow feature map, and a deep-level prediction head P5 which is removed to reduce the effect of excessive down-sampling. To mitigate the mismatch between the desired ground truth box and the prediction box, the SCYLLA-IoU function is used in the regression loss to speed up the training convergence process. Moreover, the proposed YOLOv7-UAV algorithm is quantified and compiled in the Vitis-AI development environment and validated in terms of power consumption and hardware resources on the FPGA platform. The experimental results show that the mean average precision of YOLOv7-UAV is improved by 33% compared to traditional YOLOv7, and the FPGA implementation improves the energy efficiency by 12 times compared to the GPU.