Object detection using UAV images is a current research focus in the field of computer vision, with frequent advancements in recent years. However, many methods are ineffective for challenging UAV images that feature uneven object scales, sparse spatial distribution, and dense occlusions. We propose a new algorithm for detecting small objects in UAV images, called ATBHC-YOLO. Firstly, the MS-CET module has been introduced to enhance the model’s focus on global sparse features in the spatial distribution of small objects. Secondly, the BHC-FB module is proposed to address the large-scale variance of small objects and enhance the perception of local features. Finally, a more appropriate loss function, WIoU, is used to penalise the quality variance of small object samples and further enhance the model’s detection accuracy. Comparison experiments on the DIOR and VEDAI datasets validate the effectiveness and robustness of the improved method. By conducting experiments on the publicly available UAV benchmark dataset Visdrone, ATBHC-YOLO outperforms the state-of-the-art method(YOLOv7) by 3.5%.

使用无人机图像进行目标检测是计算机视觉领域当前的研究重点，近年来进展频繁。然而，许多方法对于具有挑战性的无人机图像无效，这些图像具有不均匀的对象比例、稀疏的空间分布和密集的遮挡。我们提出了一种检测无人机图像中小物体的新算法，称为 ATBHC-YOLO。首先，引入了 MS-CET 模块，以增强模型对小天体空间分布中全局稀疏特征的关注;其次，提出 BHC-FB 模块来解决小目标的大尺度变化，增强对局部特征的感知;最后，使用更合适的损失函数 WIoU 对小目标样本的质量方差进行惩罚，进一步提高模型的检测精度。在 DIOR 和 VEDAI 数据集上的比较实验验证了改进方法的有效性和稳健性。通过在公开可用的无人机基准数据集 Visdrone 上进行实验，ATBHC-YOLO 的性能比最先进的方法 （YOLOv7） 高出 3.5%。