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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A link to the dataset will be provided in the final manuscript.
References
Yu, X., Yang, G., Jones, S., Saniie, J.: AR marker aided obstacle localization system for assisting visually impairedn. In: IEEE International Conference on Electro/Information Technology (EIT), pp. 271–276 (2018)
Diamantis, D.E., Iakovidis, D.K., Koulaouzidis, A.: Look-behind fully convolutional neural network for computer-aided endoscopy. Biomed. Signal Process. Control 49, 192–201 (2019)
Se, S., Brady, M.: Vision-based detection of staircases. In: Fourth Asian Conference on Computer Vision ACCV, vol. 1, pp. 535–540 (2000)
Hesch, J.A., Mariottini, G.L., Roumeliotis, S.I.: Descending-stair detection, approach, and traversal with an autonomous tracked vehicle. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5525–5531 (2010)
Lee, Y.H., Leung, T.-S., Medioni, G.: Real-time staircase detection from a wearable stereo system. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3770–3773 (2012)
Maohai, L., Han, W., Lining, S., Zesu, C.: A robust vision-based method for staircase detection and localization. Cogn. Process. 15(2), 173–194 (2014)
Bouhamed, S.A., Kallel, I.K., Masmoudi, D.S.: Stair case detection and recognition using ultrasonic signal. In: 2013 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 672–676 (2013)
Pérez-Yus, A., López-Nicolás, G., Guerrero, J.J.: Detection and modelling of staircases using a wearable depth sensor. In: Agapito, L., Bronstein, Michael M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8927, pp. 449–463. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16199-0_32
Ciobanu, A., Morar, A., Moldoveanu, F., Petrescu, L., Ferche, O., Moldoveanu, A.: Real-time indoor staircase detection on mobile devices. In: 2017 21st International Conference on Control Systems and Computer Science (CSCS), pp. 287–293 (2017)
LeCun, Y., et al.: LeNet-5, convolutional neural networks, p. 20 (2015). http://yann.lecun.com/exdb/lenet
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5 mb model size, arXiv preprint arXiv:1602.07360 (2016)
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861 (2017)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. ArXiv e-prints, July 2017
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Chollet, F.: Xception: Deep learning with depthwise separable convolutions, arXiv preprint, pp. 1610–2357 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_26
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vision 77(1–3), 157–173 (2008)
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: Large-scale scene recognition from abbey to zoo. In: IEEE Conference on 2010 Computer Vision and Pattern Recognition (CVPR), pp. 3485–3492 (2010)
Flickr Inc., “Find your inspiration. | Flickr.” 2019
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Chollet, F.: Keras. (2015)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
Sanders, J., Kandrot, E.: CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional, (2010)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-20257-6_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20256-9
Online ISBN: 978-3-030-20257-6
eBook Packages: Computer ScienceComputer Science (R0)