Abstract: Small-target detection in aerial imagery is challenged by tiny object sizes, complex backgrounds and large scale variations, while existing detectors still underperform in feature extraction, multi-scale fusion and small-target awareness; to address these limitations we present MA-DETR, an improved RT-DETR-based aerial small-target detection algorithm. First, a Dual Adaptive Perception Network (DAPN) is embedded in the backbone, leveraging a spatial–scale separation module and a dual adaptive pooling mechanism to enhance perception across diverse scales. Second, an Adaptive Multi-Scale Feature Fusion Network (AMSFN) is designed with a multi-module collaborative architecture that establishes a bidirectional multi-path feature transmission mechanism to boost small-target representation. Additionally, a small-target detection layer based on Adaptive Wavelet Convolution (AWC) is introduced, serially combining wavelet convolution with a remote-sensing anchor attention mechanism to strengthen small-target features in both the frequency and spatial domains. Finally, a CF-CGDL loss integrating a core focusing mechanism and a corner geometric distance loss is proposed to refine bounding-box regression. Experiments on VisDrone2019 yield 43.5 % mAP@50, outperforming the baseline by 6.4 % while reducing parameters by 1.1 × 10⁶; generalization tests on DOTA v1.0 and RSOD reach 71.8 % and 95.5 % mAP@50, gains of 3.1% and 7.1 % respectively, demonstrating the method’s effectiveness and robustness.

摘要： 航拍图像中的小目标检测面临着目标尺寸小、背景复杂、多尺度变化等挑战，现有检测算法在特征提取、多尺度融合和小目标感知方面存在不足，针对上述问题，研究提出一种基于改进RT-DETR的航拍小目标检测算法MA-DETR。首先，在主干网络中设计双重自适应感知网络DAPN，通过空间尺度分离模块和双重自适应池化机制，增强网络对不同尺度目标的感知能力。其次，设计自适应多尺度特征融合网络AMSFN，通过多模块协同架构，构建双向多路径特征传递机制，提升小目标特征的表达能力。并且提出基于自适应小波卷积AWC的小目标检测层，通过小波卷积和遥感锚定注意力的串行设计，在频域和空域双重增强小目标特征。最后，设计CF-CGDL损失函数，融合核心聚焦机制与角点几何距离损失，改善边界框回归精度。在VisDrone2019数据集上的实验结果表明，改进算法的mAP@50达到了43.5%，较基准模型提升了6.4%，参数量减少1.1×106，泛化实验结果显示，在DOTA v1.0和RSOD数据集上的mAP@50也分别达到了71.8%和95.5%，较基准模型提高了3.1%和7.1%，验证了该方法的有效性和鲁棒性。