• 1987 (Vol.1)

Improvement of a line segment detector based on a neural network by adding engineering features

© 2021 L. A. Erlygin, L. M. Teplyakov

Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Institutsky per., 9, Moscow region, Russia
Institute for Information Transmission Problems. A.A. Kharkevich Russian Academy of Sciences, 127051 Moscow, Bolshoi Karetny lane, 19, Russia

Received 02 Nov 2020

In this work, we study the problem of line segment detection. In the recent studies of this problem, it was shown that neural network-based methods are able to outperform classical algorithms in accuracy, yet high computational complexity of neural networks limits their usage in real-world applications. In our work, we propose to fuse a neural network with a classical algorithm to compensate their flaws, by (i) feeding into the network not only an image, but also classical algorithm’s output and (ii) by combining their predictions. We expect these modifications to simplify the problem faced by the neural network, resulting in higher accuracy in smaller network. Proposed method provides 0.72 and 0.66 F – measure on Wireframe and York datasets respectively with 14 FPS on CPU.

Key words: artificial neural networks, line segment detection, contour detection, Unet, Canny

DOI: 10.31857/S0235009221010042

Cite: Erlygin L. A., Teplyakov L. M. Uluchshenie neirosetevogo detektora otrezkov putem dobavleniya inzhenernykh priznakov [Improvement of a line segment detector based on a neural network by adding engineering features]. Sensornye sistemy [Sensory systems]. 2021. V. 35(1). P. 50–54 (in Russian). doi: 10.31857/S0235009221010042


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