Line detection via a lightweight CNN with a Hough layer

Lev Teplyakov; Kirill Kaymakov; Evgeny Shvets; Dmitry Nikolaev

doi:10.1117/12.2587167

4 January 2021 Line detection via a lightweight CNN with a Hough layer

Lev Teplyakov, Kirill Kaymakov, Evgeny Shvets, Dmitry Nikolaev

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051B (2021) https://doi.org/10.1117/12.2587167
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

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.

Citation Download Citation

Lev Teplyakov, Kirill Kaymakov, Evgeny Shvets, and Dmitry Nikolaev "Line detection via a lightweight CNN with a Hough layer", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051B (4 January 2021); https://doi.org/10.1117/12.2587167

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