  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.
