(1)背景:无人机(UAV)图像中的小物体往往分散在图像的各个区域,比如角落,并且可能被更大的物体阻挡,以及易受图像噪声的影响。此外,由于尺寸小,这些物体在图像中占据有限的区域,导致缺乏有效的检测特征。(2)方法:为了解决无人机影像中的小目标检测问题,我们介绍了一种新的算法,称为高分辨率特征金字塔网络基于Mamba的YOLO(HRMamba-YOLO)。该算法利用了高分辨率网络(HRNet)的优势,EfficientVMamba,和YOLOv8,集成了双空间金字塔池(双SPP)模块,高效曼巴模块(EMM),和融合曼巴模块(FMM),以增强特征提取和捕获上下文信息。此外,一种新的多尺度特征融合网络,高分辨率特征金字塔网络(HRFPN),和FMM改进了特征交互,增强了小目标检测的性能。(3)结果:对于VisDroneDET数据集,与YOLOv8-m相比,该算法的平均平均精度(mAP)提高了4.4%。实验结果表明,HRMamba实现了37.1%的mAP,超过YOLOv8-m3.8%(Dota1.5数据集)。对于UCAS_AOD数据集和DIOR数据集,我们的模型的MAP比YOLOv8-m模型高1.5%和0.3%,分别。公平地说,所有模型均在没有预训练模型的情况下进行训练.(4)结论:本研究不仅突出了HRMamba-YOLO在小目标检测任务中的卓越性能和效率,而且为未来的研究提供了创新的解决方案和有价值的见解。
(1) Background: Small objects in Unmanned Aerial Vehicle (UAV) images are often scattered throughout various regions of the image, such as the corners, and may be blocked by larger objects, as well as susceptible to image noise. Moreover, due to their small size, these objects occupy a limited area in the image, resulting in a scarcity of effective features for detection. (2) Methods: To address the detection of small objects in UAV imagery, we introduce a novel algorithm called High-Resolution Feature Pyramid Network Mamba-Based
YOLO (HRMamba-
YOLO). This algorithm leverages the strengths of a High-Resolution Network (HRNet), EfficientVMamba, and YOLOv8, integrating a Double Spatial Pyramid Pooling (Double SPP) module, an Efficient Mamba Module (EMM), and a Fusion Mamba Module (FMM) to enhance feature extraction and capture contextual information. Additionally, a new Multi-Scale Feature Fusion Network, High-Resolution Feature Pyramid Network (HRFPN), and FMM improved feature interactions and enhanced the performance of small object detection. (3) Results: For the VisDroneDET dataset, the proposed algorithm achieved a 4.4% higher Mean Average Precision (mAP) compared to YOLOv8-m. The experimental results showed that HRMamba achieved a mAP of 37.1%, surpassing YOLOv8-m by 3.8% (Dota1.5 dataset). For the UCAS_AOD dataset and the DIOR dataset, our model had a mAP 1.5% and 0.3% higher than the YOLOv8-m model, respectively. To be fair, all the models were trained without a pre-trained model. (4) Conclusions: This study not only highlights the exceptional performance and efficiency of HRMamba-
YOLO in small object detection tasks but also provides innovative solutions and valuable insights for future research.