Abstract: Unmanned aerial vehicle (UAV) object detection has been playing a crucial role in such fields as intelligent transportation and environmental monitoring. Nevertheless, due to the constraints of multiple factors including target size variations and diverse shooting angles, small object detection in UAV aerial imagery is confronted with prominent problems of drastic scale changes and easy feature attenuation. To tackle the aforementioned issues, this study proposes an improved object detection algorithm for UAV aerial-view scenarios based on YOLOv11n, namely DBD-YOLO. In the feature extraction stage, the DWR multi-scale structure is introduced, which combines dilated convolutions with multiple dilation rates and adaptive channel allocation. This structure can effectively expand the receptive field with low computational overhead and enhance the contextual representation of small objects. In the neck network, a new P2 feature layer is incorporated into the feature fusion process. Bi-directional Feature Pyramid Network (BiFPN) is adopted to realize cross-scale bidirectional weighted fusion, so as to improve the collaboration efficiency between shallow detailed features and deep semantic features. Meanwhile, traditional upsampling is replaced by Dysample point resampling, which not only reduces memory consumption and latency but also maintains fine-grained features. Finally, the DynamicHead, a dynamic adaptive detection head, is introduced. It integrates scale awareness, spatial awareness, and task awareness into a unified framework, and effectively applies the attention mechanism in the object detection head, thereby comprehensively improving the classification and localization performance of small objects in aerial imagery. Experimental results on the VisDrone2019-DET dataset show that the proposed DBD-YOLO algorithm achieves 45.2% in mAP50 and 27.4% in mAP50-95, representing an increase of 12.1% and 8.1% compared with the baseline , respectively. At the same time, the number of model parameters remains roughly at the same level as the baseline, realizing a dual breakthrough in both detection accuracy and computational efficiency.

摘要： 无人机目标检测在智能交通、环境监控等领域发挥着重要作用，然而受目标尺寸大小、拍摄角度等多种因素限制，使航拍小目标检测面临尺度变化剧烈与特征易衰减的问题。针对上述问题，提出一种改进YOLOv11n的无人机航拍视角下的目标检测算法：DBD-YOLO。在特征提取阶段引入融合多膨胀率空洞卷积与自适应通道分配的DWR多尺度结构，在低计算开销下有效扩展感受野并增强小目标上下文表征；在颈部网络中新增P2特征层参与特征融合流程，采用BiFPN实现跨尺度双向加权融合，以提升浅层细节与深层语义协同效率，并以Dysample点重采样替代传统上采样，在降低显存与时延的同时保持细粒度特征；最终引入动态自适应检测头DynamicHead，将尺度感知、空间感知和任务感知融合到一个统一的框架中，并在目标检测头中有效地应用注意力机制，整体提升航拍小目标检测的分类与定位性能。所提算法在VisDrone2019-DET数据集上的mAP50、mAP50-95分别达到了45.2%、27.4%，相较于基准算法分别提升了12.1%、8.1%，同时模型参数量基本保持同一水平，实现了精度与效率的双重突破。