Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images. However, detecting power lines in aerial images is difficult,as the foreground data(i.e, power lines) is small and the background information is abundant.To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm explicitly designed to detect power lines in aerial images. We presuppose that it is advantageous to train an efficient Transformer model with sufficient feature extraction using a convolutional neural network(CNN) with a strong inductive bias.With this goal in mind, we introduce a heavy token encoder that performs overlapping feature remodeling and tokenization. The encoder comprises a pyramid CNN feature extraction module and a power line feature enhancement module.After successful local feature extraction for power lines, feature fusion is conducted.Then,the Transformer block is used for global modeling. The final segmentation result is achieved by amalgamating local and global features in the decode head.Moreover, we demonstrate the importance of the joint multi-weight loss function in power line segmentation. Our experimental results show that our proposed method outperforms all state-of-the-art methods in power line segmentation on the publicly accessible TTPLA dataset.

翻译：无人机（UAV）常用于电力线巡检并采集高分辨率航拍图像。然而，航拍图像中的电力线检测面临显著挑战：前景数据（即电力线）占比极小，而背景信息极为丰富。为解决该问题，本文提出DUFormer——一种专为航拍图像电力线检测设计的语义分割算法。我们预设核心思路为：利用具有强归纳偏置的卷积神经网络（CNN）进行充分特征提取后，再结合高效的Transformer模型进行训练将更为有利。基于此，我们引入重型令牌编码器，执行重叠特征重构与令牌化操作。该编码器由金字塔CNN特征提取模块与电力线特征增强模块构成。在完成电力线局部特征有效提取后进行特征融合，继而采用Transformer模块实现全局建模。通过解码头融合局部与全局特征，最终获得分割结果。此外，我们验证了联合多权重损失函数在电力线分割中的关键作用。实验结果表明，在公开TTPLA数据集上，所提方法在电力线分割任务中全面超越现有最优方法。