  Accurate segmentation of power lines in various aerial images is very
important for UAV flight safety. The complex background and very thin
structures of power lines, however, make it an inherently difficult task in
computer vision. This paper presents PLGAN, a simple yet effective method based
on generative adversarial networks, to segment power lines from aerial images
with different backgrounds. Instead of directly using the adversarial networks
to generate the segmentation, we take their certain decoding features and embed
them into another semantic segmentation network by considering more context,
geometry, and appearance information of power lines. We further exploit the
appropriate form of the generated images for high-quality feature embedding and
define a new loss function in the Hough-transform parameter space to enhance
the segmentation of very thin power lines. Extensive experiments and
comprehensive analysis demonstrate that our proposed PLGAN outperforms the
prior state-of-the-art methods for semantic segmentation and line detection.
