Power lines pose a significant safety threat to unmanned aerial vehicles
(UAVs) operating at low altitudes. However, detecting power lines in aerial
images is challenging due to the small size of the foreground data (i.e., power
lines) and the abundance of background information. To address this challenge,
we propose DUFormer, a semantic segmentation algorithm designed specifically
for power line detection in aerial images. We assume that performing sufficient
feature extraction with a convolutional neural network (CNN) that has a strong
inductive bias is beneficial for training an efficient Transformer model. To
this end, we propose a heavy token encoder responsible for overlapping feature
re-mining and tokenization. The encoder comprises a pyramid CNN feature
extraction module and a power line feature enhancement module. Following
sufficient feature extraction for power lines, the feature fusion is carried
out, and then the Transformer block is used for global modeling. The final
segmentation result is obtained by fusing local and global features in the
decode head. Additionally, we demonstrate the significance of the joint
multi-weight loss function in power line segmentation. The experimental results
demonstrate that our proposed method achieves the state-of-the-art performance
in power line segmentation on the publicly available TTPLA dataset.

该论文提出了一种用于航空图像中的电力线检测的语义分割算法 DUFormer，该算法采用具有强归纳偏差的卷积神经网络，以及一个重量级令牌编码器，负责重叠特征再挖掘和标记化，结果显示该算法在公开可用的 TTPLA 数据集上的电力线分割任务中达到了最先进水平的表现。

DUFormer：航拍图像中电力线路分割的一种新型架构

DUFormer: A Novel Architecture for Power Line Segmentation of Aerial  Images

In low-altitude Unmanned Aerial Vehicle (UAV) flights, power lines are
considered as one of the most threatening hazards and one of the most difficult
obstacles to avoid. In recent years, many vision-based techniques have been
proposed to detect power lines to facilitate self-driving UAVs and automatic
obstacle avoidance. However, most of the proposed methods are typically based
on a common three-step approach: (i) edge detection, (ii) the Hough transform,
and (iii) spurious line elimination based on power line constrains. These
approaches not only are slow and inaccurate but also require a huge amount of
effort in post-processing to distinguish between power lines and spurious
lines. In this paper, we introduce LS-Net, a fast single-shot line-segment
detector, and apply it to power line detection. The LS-Net is by design fully
convolutional and consists of three modules: (i) a fully convolutional feature
extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the
unavailability of large datasets with annotations of power lines, we render
synthetic images of power lines using the Physically Based Rendering (PBR)
approach and propose a series of effective data augmentation techniques to
generate more training data. With a customized version of the VGG-16 network as
the backbone, the proposed approach outperforms existing state-of-the-art
approaches. In addition, the LS-Net can detect power lines in near real-time
(20.4 FPS). This suggests that our proposed approach has a promising role in
automatic obstacle avoidance and as a valuable component of self-driving UAVs,
especially for automatic autonomous power line inspection.

本文介绍了 LS-Net 单次拍摄行段检测器，将其应用于无人机的输电线路检测中，该方法不仅快速准确，而且通过物理渲染技术生成的合成图像进行数据增强，使其在自主避障和自主飞行方面具有广泛的应用前景。