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Staircase Detection Using a Lightweight Look-Behind Fully Convolutional Neural Network

Part of the Communications in Computer and Information Science book series (CCIS,volume 1000)

Abstract

Staircase detection in natural images has several applications in the context of robotics and visually impaired navigation. Previous works are mainly based on handcrafted feature extraction and supervised learning using fully annotated images. In this work we address the problem of staircase detection in weakly labeled natural images, using a novel Fully Convolutional neural Network (FCN), named LB-FCN light. The proposed network is an enhanced version of our recent Look-Behind FCN (LB-FCN), suitable for deployment on mobile and embedded devices. Its architecture features multi-scale feature extraction, depthwise separable convolutions and residual learning. To evaluate its computational and classification performance, we have created a weakly-labeled benchmark dataset from publicly available images. The results from the experimental evaluation of LB-FCN light indicate its advantageous performance over the relevant state-of-the-art architectures.

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Notes

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    A link to the dataset will be provided in the final manuscript.

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Acknowledgments

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code:Τ1EDK-02070). It was also supported by the Onassis Foundation - Scholarship ID: G ZΟ 004-1/2018-2019. The Titan X used for this research was donated by the NVIDIA Corporation.

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Correspondence to Dimitrios E. Diamantis .

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Diamantis, D.E., Koutsiou, DC.C., Iakovidis, D.K. (2019). Staircase Detection Using a Lightweight Look-Behind Fully Convolutional Neural Network. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_45

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  • DOI: https://doi.org/10.1007/978-3-030-20257-6_45

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