Wang Chengcheng, Han Yuqi, Yang Chenggui, Wu Mingjie, Chen Zaiqing, Yun Lijun, Jin Xuesong
School of Information, Yunnan Normal University, Kunming, 650500, China.
Computer Vision and Intelligent Control Technology Engineering Research Center, Yunnan Provincial Department of Education, Kunming, 650500, China.
Sci Rep. 2025 May 14;15(1):16741. doi: 10.1038/s41598-025-99634-0.
Images captured from a drone's perspective are significantly impacted in terms of target detection algorithm performance due to the notable differences in target scales and the presence of numerous small target objects lacking detailed information. This paper proposes a Remote Sensing Small Target Detector (CF-YOLO) based on the YOLOv11 model to address the challenges of small target detection. Firstly, addressing the issue of small target information loss that may arise from hierarchical convolutional structures, we conduct in-depth research on the Path Aggregation Network (PAN) and innovatively propose a Cross-Scale Feature Pyramid Network (CS-FPN). Secondly, to overcome the problems of positional information deviation and feature redundancy during multi-scale feature fusion, we design a Feature Recalibration Module (FRM) and a Sandwich Fusion Module. We advocate for initial feature fusion through the FRM module, followed by feature enhancement using the Sandwich module. Finally, we optimize and reconstruct the model using the RFAConv module and LSDECD detection head. Experiments show that on the public VisDrone dataset, TinyPerson dataset, and HIT-UAV dataset, CF-YOLO improves the mAP50 by 12.7%, 10.1%, and 3.5%, respectively, compared to the baseline model. Compared to other methods, CF-YOLO demonstrates superior performance.
由于目标尺度存在显著差异以及存在大量缺乏详细信息的小目标物体，从无人机视角拍摄的图像在目标检测算法性能方面受到显著影响。本文提出一种基于YOLOv11模型的遥感小目标检测器（CF-YOLO），以应对小目标检测的挑战。首先，针对分层卷积结构可能导致的小目标信息丢失问题，我们对路径聚合网络（PAN）进行深入研究，并创新性地提出跨尺度特征金字塔网络（CS-FPN）。其次，为克服多尺度特征融合过程中的位置信息偏差和特征冗余问题，我们设计了特征重新校准模块（FRM）和三明治融合模块。我们主张先通过FRM模块进行初始特征融合，然后使用三明治模块进行特征增强。最后，我们使用RFAConv模块和LSDECD检测头对模型进行优化和重构。实验表明，在公共VisDrone数据集、TinyPerson数据集和HIT-UAV数据集上，与基线模型相比，CF-YOLO分别将mAP50提高了12.7%、10.1%和3.5%。与其他方法相比，CF-YOLO表现出卓越的性能。
