Miao Jun, Liu Peng, Qiao Yuanhua
College of Computer Science, Beijing Information Science and Technology University, Beijing, 102206, China.
College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China.
Sci Rep. 2025 Jul 2;15(1):22455. doi: 10.1038/s41598-025-05772-w.
Tiny object detection in aerial image is crucial for urban planning and environmental monitoring. However, unpredictable orientation and lack of distinctive features pose challenges in sample assignment, often resulting in mismatch and inconsistency between anchors and priors. To address this, we introduce the multi-factor consideration sample assignment (MCSA) mechanism, which ensures the assignment of superior positive samples for objects with orientation. Initially, we craft a dynamic prior block (DPB) to facilitate the dynamic alignment of priors with objects. Subsequently, we introduce an anchor assessment metric that assesses all potential anchors thoroughly. Lastly, we deploy a dynamic Gaussian mixture model (DGMM) to eliminate subpar samples. Our method outperforms most current techniques on four datasets: DIOR-R, DOTA-v2.0, DOTA-v1.5 and DOTA-1.0. Notably, we achieve the mAP of 51.86% on the DOTA-v2.0 dataset, surpassing the baseline by 5.18 percentage points. By distributing priors dynamically and selecting the most compatible positive samples based on the highest matching scores, our approach ensures precise sample assignment, consequently enhancing detection precision.
航空图像中的微小目标检测对于城市规划和环境监测至关重要。然而，不可预测的方向和缺乏显著特征给样本分配带来了挑战，常常导致锚点和先验之间的不匹配和不一致。为了解决这个问题，我们引入了多因素考虑样本分配（MCSA）机制，该机制确保为有方向的目标分配优质正样本。首先，我们精心设计了一个动态先验块（DPB），以促进先验与目标的动态对齐。随后，我们引入了一种锚点评估指标，对所有潜在锚点进行全面评估。最后，我们部署了一个动态高斯混合模型（DGMM）来消除不合格样本。我们的方法在四个数据集上优于大多数现有技术：DIOR-R、DOTA-v2.0、DOTA-v1.5和DOTA-1.0。值得注意的是，我们在DOTA-v2.0数据集上实现了51.86%的平均精度均值（mAP），比基线高出5.18个百分点。通过动态分布先验并根据最高匹配分数选择最兼容的正样本，我们的方法确保了精确的样本分配，从而提高了检测精度。
