  Line detection is an important computer vision task traditionally solved by
Hough Transform. With the advance of deep learning, however, trainable
approaches to line detection became popular. In this paper we propose a
lightweight CNN for line detection with an embedded parameter-free Hough layer,
which allows the network neurons to have global strip-like receptive fields. We
argue that traditional convolutional networks have two inherent problems when
applied to the task of line detection and show how insertion of a Hough layer
into the network solves them. Additionally, we point out some major
inconsistencies in the current datasets used for line detection.
