【摘要】 输电杆塔检测任务面临着目标特征复杂多变，且样本数据少、特征分布不均衡的问题。输电杆塔具有细高形的结构特点，在不同成像条件的卫星影像中呈现显著的特征差异，常规数据增强方法难以有效模拟其在不同成像条件和不同背景下输电杆塔特征。针对这一问题，本文提出了基于成像条件的数据增强方法，从成像角度和成像背景两个方面实现对输电杆塔目标数据的增强。成像角度增强方法通过模拟卫星视角变化生成不同拍摄角度下的输电杆塔图像；成像背景增强方法通过自适应加权融合边缘修复与泊松融合两种方法生成不同背景下的目标图像。在Faster R-CNN、Retinanet和YOLOv3模型下对本文方法进行实验，检测精度相比传统方法均有明显提升。结果表明：该方法相较传统数据增强方法能更好地提升模型对输电杆塔目标的检测能力与定位精度。更多还原

【Abstract】 The task of transmission tower detection is faced with the problem of complex target characteristics, small sample data and unbalanced feature distribution. In view of the feature difference of transmission tower under different imaging conditions, a data augmentation method based on imaging conditions is proposed in this paper. The method realizes the data augmentation of transmission tower target from two aspects of imaging angle and imaging background. The imaging angle augmentation method can generate transmission tower images at different shooting angles by simulating satellite angle changes. The imaging background augmentation method generates target images under different backgrounds by adaptively merge the method of edge inpainting and Poisson fusion. Experimental validation on Faster R-CNN, RetinaNet, and YOLOv3 frameworks demonstrates that our method achieves significant improvements in detection accuracy compared with conventional approaches. The results indicate that the proposed methodology outperforms traditional data augmentation techniques in enhancing both the detection capability and localization precision for transmission tower targets.更多还原