Small object detection in aerial images suffers from severe information degradation during feature extraction due to limited pixel representations, where shallow spatial details fail to align effectively with semantic information, leading to frequent misses and false positives. Existing FPN-based methods attempt to mitigate these losses through post-processing enhancements, but the reconstructed details often deviate from the original image information, impeding their fusion with semantic content. To address this limitation, we propose PRNet, a real-time detection framework that prioritizes the preservation and efficient utilization of primitive shallow spatial features to enhance small object representations. PRNet achieves this via two modules:the Progressive Refinement Neck (PRN) for spatial-semantic alignment through backbone reuse and iterative refinement, and the Enhanced SliceSamp (ESSamp) for preserving shallow information during downsampling via optimized rearrangement and convolution. Extensive experiments on the VisDrone, AI-TOD, and UAVDT datasets demonstrate that PRNet outperforms state-of-the-art methods under comparable computational constraints, achieving superior accuracy-efficiency trade-offs.

翻译：航空图像中的小目标检测由于像素表示有限，在特征提取过程中存在严重的信息退化问题，浅层空间细节难以与语义信息有效对齐，导致频繁的漏检与误检。现有基于FPN的方法试图通过后处理增强来缓解这些损失，但重建的细节常偏离原始图像信息，阻碍其与语义内容的融合。为克服这一局限性，我们提出PRNet——一种实时检测框架，其核心在于优先保持并高效利用原始浅层空间特征以增强小目标表示。PRNet通过两个模块实现这一目标：渐进式优化颈部（PRN）通过骨干网络复用与迭代优化实现空间-语义对齐，以及增强型切片采样（ESSamp）通过优化的重排与卷积操作在下采样过程中保留浅层信息。在VisDrone、AI-TOD和UAVDT数据集上的大量实验表明，在可比计算约束下，PRNet优于现有先进方法，实现了更优的精度-效率平衡。