  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.
