Zhou Shilong, Zhou Haijin, Qian Lei
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
University of Science and Technology of China, Hefei, 230026, China.
Sci Rep. 2025 Mar 18;15(1):9255. doi: 10.1038/s41598-025-92344-7.
Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. In addition, improving model performance requires a delicate balance between improving accuracy and managing computational complexity. To address these challenges, we propose the SMA-YOLO algorithm. First, we introduce the Non-Semantic Sparse Attention (NSSA) mechanism in the backbone network, which efficiently extracts non-semantic features related to the task, thus improving the model's sensitivity to small objects. In the model's throat, we design a Bidirectional Multi-Branch Auxiliary Feature Pyramid Network (BIMA-FPN), which integrates high-level semantic information with low-level spatial details, improving small object detection while expanding multi-scale receptive fields. Finally, we incorporate a Channel-Space Feature Fusion Adaptive Head (CSFA-Head), which fully handles multi-scale features and adaptively handles consistency problems of different scales, further improving the robustness of the model in complex scenarios. Experimental results on the VisDrone2019 dataset show that SMA-YOLO achieves a 13% improvement in mAP compared to the baseline model, demonstrating exceptional adaptability in small object detection tasks for remote sensing imagery. These results provide valuable insights and new approaches to further advance research in this area.
在复杂的遥感环境中检测小目标存在重大挑战，包括局部空间信息提取不足、特征融合僵硬以及全局特征表示有限。此外，提高模型性能需要在提高准确性和管理计算复杂度之间取得微妙平衡。为应对这些挑战，我们提出了SMA-YOLO算法。首先，我们在主干网络中引入了非语义稀疏注意力（NSSA）机制，该机制有效提取与任务相关的非语义特征，从而提高模型对小目标的敏感度。在模型的瓶颈处，我们设计了双向多分支辅助特征金字塔网络（BIMA-FPN），它将高级语义信息与低级空间细节相结合，在扩大多尺度感受野的同时提高小目标检测能力。最后，我们加入了通道-空间特征融合自适应头（CSFA-Head），它能充分处理多尺度特征并自适应处理不同尺度的一致性问题，进一步提高模型在复杂场景中的鲁棒性。在VisDrone2019数据集上的实验结果表明，与基线模型相比，SMA-YOLO的平均精度均值（mAP）提高了13%，在遥感图像小目标检测任务中展现出卓越的适应性。这些结果为该领域的进一步研究提供了有价值的见解和新方法。
