基于U形特征融合的遥感图像目标检测方法

U-SHAPE FUSION FEATURE BASED OBJECT DETECTION METHOD FOR REMOTE SENSING IMAGES

  • 摘要: 由于遥感图像具有视野大、目标小等特殊性,如何准确地检测遥感图像的目标仍然是具有挑战性的问题。基于YOLOv3进行改进,提出新方法U-YOLO。改进YOLOv3的预选框的选取方法,解决预选框选取不平衡的问题。提出U形特征提取模块来提取更深层次的特征,提高检测的效果。提出应用于损失函数的面积因子,改善小目标检测难的问题。分别在NWPU VHR-10数据集和RSOD数据集进行实验,实验结果表明,该方法分别比原始的YOLOv3提高了0.079和0.065。

     

    Abstract: Due to the particularity of remote sensing image, such as wide field of vision, small target, how to quickly JP3and accurately detect targets in remote sensing images is still a challenging problem. A new method based on improved YOLOv3, U-YOLO, is presented. The selection method of anchor box was improved, and the problem of unbalanced selection of pre-selection box was solved. A U-shaped feature extraction module was proposed to extract deeper features and improve the detection effect. JPThe area factor applied to the loss function was put forward, which improved the difficulty of small target detection. The experiments were conducted on the NWPU VHR-10 dataset and RSOD dataset. Experimental results show that this method is 0.079 and 0.065 higher than the original YOLOv3 in the two groups of experiments, respectively.

     

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