Advanced communications and networks have greatly improved the user experience, and unmanned aerial vehicle (UAV) are an important technology that supports people's daily life and military activities. Since target detection in UAV images is complicated by a complex background, small targets, and target occlusion, the detection accuracy of the You Only Look Once(YOLO) v4 algorithm is relatively low. Therefore, hollow convolution is used to resample the feature image to improve the feature extraction and target detection performance. In addition, the ultra-lightweight subspace attention mechanism (ULSAM) is used to derive different attention feature maps for each subspace of the feature map for multi-scale feature representation. Finally, soft non-maximum suppression (Soft-NMS) is introduced to minimize the occurrence of missed targets due to occlusion. The experimental results prove that the proposed UAV image target detection model (YOLOv4_Drone) has 5% improved to the YOLOv4 algorithm, demonstrating the effectiveness of the method.

先进的通信和网络极大地提升了用户体验，无人机是支撑人们日常生活和军事活动的重要技术。由于无人机图像中目标检测复杂，背景复杂，目标小，目标遮挡，You Only Look Once(YOLO) v4算法的检测精度相对较低。因此，采用空心卷积对特征图像进行重采样，以提高特征提取和目标检测性能。此外，超轻量级子空间注意力机制（ULSAM）用于为特征图的每个子空间导出不同的注意力特征图，用于多尺度特征表示。最后，引入了软非最大抑制（Soft-NMS）以最大限度地减少由于遮挡而丢失目标的发生。实验结果证明所提出的无人机图像目标检测模型（YOLOv4_Drone）比YOLOv4算法提高了5%，证明了该方法的有效性。