Test results of different models on TinyPerson.

In UAV aerial photography scenarios, targets exhibit characteristics such as multi-scale distribution, a high proportion of small targets, complex occlusions, and strong background interference. These characteristics impose high demands on detection algorithms in terms of fine-grained feature extraction, cross-scale fusion capability, and occlusion resistance.The YOLOv11s model has significant limitations in practical applications: its feature extraction module has a single semantic representation, the traditional feature pyramid network has limited capability to detect multi-scale targets, and it lacks an effective feature compensation mechanism when targets are occluded.To address these issues, we propose a UAV aerial small target detection algorithm named UAS-YOLO (Universal Inverted Bottleneck with Adaptive BiFPN and Separated and Enhancement Attention module YOLO), which incorporates three key optimizations. First, an Adaptive Bidirectional Feature Pyramid Network (ABiFPN) is designed as the Neck structure. Through cross-scale connections and dynamic weighted fusion, ABiFPN adjusts weight allocation based on target scale characteristics, focusing on enhancing feature integration for scales related to small targets and improving multi-scale feature representation capability. Second, a Separated and Enhancement Attention Module (SEAM) is introduced to replace the original SPPF module. This module focuses on key target regions, enhances effective feature responses in unoccluded areas, and specifically compensates for information loss in occluded regions, thereby improving the detection stability of occluded small targets. Third, a Universal Inverted Bottleneck (UIB) structure is proposed, which is fused with the C3K2 module to form the C3K2_UIB module. By leveraging dynamic channel attention and spatial feature recalibration, C3K2_UIB suppresses background noise; although this increases parameters by 34%, it achieves improved detection accuracy through efficient feature selection, striking a balance between accuracy and complexity.Experimental results show that on the VisDrone2019 dataset and the TinyPerson dataset from Kaggle, the mean Average Precision (mAP) of the algorithm is increased by 4.9 and 2.1 percentage points, respectively. Moreover, it demonstrates greater advantages compared to existing advanced algorithms, effectively addressing the challenge of small target detection in complex UAV scenarios.

在无人机（UAV）航拍场景中，目标呈现多尺度分布、小目标占比高、遮挡情况复杂、背景干扰强烈等特征。这些特性对检测算法的细粒度特征提取能力、跨尺度融合能力以及抗遮挡性能提出了较高要求。YOLOv11s模型在实际应用中存在显著局限：其特征提取模块语义表征单一，传统特征金字塔网络的多尺度目标检测能力有限，且在目标发生遮挡时缺乏有效的特征补偿机制。针对上述问题，本文提出一种名为UAS-YOLO（通用倒置瓶颈自适应双向特征金字塔分离增强注意力YOLO，Universal Inverted Bottleneck with Adaptive BiFPN and Separated and Enhancement Attention Module YOLO）的无人机航拍小目标检测算法，该算法包含三项关键优化。其一，设计自适应双向特征金字塔网络（Adaptive Bidirectional Feature Pyramid Network，ABiFPN）作为特征融合颈部（Neck）结构。通过跨尺度连接与动态加权融合，ABiFPN可根据目标尺度特性调整权重分配，重点增强与小目标相关尺度的特征整合能力，提升多尺度特征表征性能。其二，引入分离增强注意力模块（Separated and Enhancement Attention Module，SEAM）以替代原SPPF模块。该模块聚焦目标关键区域，增强非遮挡区域的有效特征响应，且可针对性补偿遮挡区域的信息丢失，从而提升遮挡小目标的检测稳定性。其三，提出通用倒置瓶颈（Universal Inverted Bottleneck，UIB）结构，并将其与C3K2模块融合形成C3K2_UIB模块。C3K2_UIB模块通过动态通道注意力与空间特征重校准抑制背景噪声；尽管该模块使参数量增加34%，但通过高效的特征选择实现了检测精度的提升，在精度与复杂度之间取得了平衡。实验结果表明，在VisDrone2019数据集与Kaggle平台的TinyPerson数据集上，所提算法的平均精度均值（mean Average Precision，mAP）分别提升4.9与2.1个百分点。此外，与现有先进算法相比，该算法展现出更优性能，可有效解决复杂无人机航拍场景下的小目标检测难题。