  We present a one-stage Fully Convolutional Line Parsing network (F-Clip) that
detects line segments from images. The proposed network is very simple and
flexible with variations that gracefully trade off between speed and accuracy
for different applications. F-Clip detects line segments in an end-to-end
fashion by predicting each line's center position, length, and angle. We
further customize the design of convolution kernels of our fully convolutional
network to effectively exploit the statistical priors of the distribution of
line angles in real image datasets. We conduct extensive experiments and show
that our method achieves a significantly better trade-off between efficiency
and accuracy, resulting in a real-time line detector at up to 73 FPS on a
single GPU. Such inference speed makes our method readily applicable to
real-time tasks without compromising any accuracy of previous methods.
Moreover, when equipped with a performance-improving backbone network, F-Clip
is able to significantly outperform all state-of-the-art line detectors on
accuracy at a similar or even higher frame rate. In other word, under same
inference speed, F-Clip always achieving best accuracy compare with other
methods. Source code https://github.com/Delay-Xili/F-Clip.
