Getting Started with Wall Segmentation

9th International Scientific Conference Technics and Informatics in Education – TIE 2022 (2022) стр. 165-170

АУТОР(И): Mihailo Bjekić, Ana Lazović

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DOI: 10.46793/TIE22.165B

САЖЕТАК:

In recent years, convolutional neural networks have been widely used in the area of semantic segmentation. In this paper, semantic segmentation network for detecting walls of indoor scenes is presented. Given an image of an indoor scene, the network automatically locates the wall regions in the image. In other words, walls are distinguished from the furniture, windows, curtains, and other possible indoor elements. Encoder-decoder structure of the semantic segmentation module is used. Specifically, PSPNet is used, one of the most common semantic segmentation algorithms. Model is trained on a new indoor scene dataset made from the publicly available ADE20K dataset, consisting of only two semantic labels: wall and no wall.

КЉУЧНЕ РЕЧИ: 

semantic segmentation; indoor scenes; encoder-decoder; ADE20K; PSPNet.

ЛИТЕРАТУРА:

  • [1] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, pp. 3523-3542, Jul. 2022.
  • [2] W. Gu, S. Bai and L. Kong, “A review on 2D instance segmentation based on deep neural networks”, Image Vis. Comput., vol. 120, Apr. 2022.
  • [3] L. Tran and M. Le, “Robust U-Net-based Road Lane Markings Detection for Autonomous Driving”, in Int. Conf. System Science Eng., Jul. 2019, pp. 62-66.
  • [4] K. Singh, R. Rawat, and A. Ashu, “Image Segmentation in Agriculture Crop and Weed Detection Using Image Processing and Deep Learning Techniques”, Int. J. Res. Eng. Science Manage., vol. 4, no. 5, pp. 235–238, Jun. 2021.
  • [5] V. Koval, D. Zahorodnia and O. Adamiv, “An Image Segmentation Method for Obstacle Detection in a Mobile Robot Environment” in 9th Int. Conf. Adv. Comput. Information Techologies, Jun. 2019, pp. 475-478.
  • [6] N. Siddique, S. Paheding, C. P. Elkin and V. Devabhaktuni, “U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications”, IEEE Access, vol. 9, pp. 82031-82057, Jun. 2021.
  • [7] B. Neupane, T. Horanont, and J. Aryal, “Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis”, Remote Sensing, vol. 13, no. 4, p. 808, Feb. 2021, Art. no. 808.
  • [8] S. Barchid, J. Mennesson, C. Djeraba, “Review of Indoor RGB-D Semantic Segmentation Convolutional Neural Network”, in Int. Conf. Content-Based Indexing, Jun. 2021.
  • [9] B. Zhou, X. Puig, S. Fidler, A. Barriuso and A. Torralba, “Scene Parsing through ADE20K Dataset”, in IEEE Conf. Comput. Vis. Pattern Recognit., Jul. 2017.
  • [10] T. Liu, Y. Wei, Y. Zhao, S. Liu and S. Wei, “Magic-Wall: Visualizing Room Decoration by Enhanced Wall Segmentation”, IEEE Trans. on Image Process., vol. 28, no. 9, pp. 4219-4232, Sept. 2019.
  • [11] P. Sharma. “Computer Vision Tutorial: A Step-by-Step Introduction to Image Segmentation Techniques (Part 1).” Analytics Vidhya. https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python (accessed Jul. 23, 2022).
  • [12] V. Badrianarayanan, A. Kendall and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, pp. 2481–2495, Jan. 2017.
  • [13] O. Ronneberger, P. Fischer and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, in Int. Conf. Med. Image Comput. Computer-Assisted Intervention, pp. 234–241, May 2015.
  • [14] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphyand A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, pp. 834–848, Apr. 2017.
  • [15] S. Jadon, „A survey of loss functions for semantic segmentation,“ in IEEE Conf. Comput. Intell. Bioinf. Comput. Biol., Oct. 2020, pp. 1-7.
  • [16] H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, „Pyramid Scene Parsing Network,“ in IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 6230-6239.
  • [17] M. Bjekić and A. Lazović. “Wall Segmentation”. GitHub.com. https://github.com/bjekic/WallSegmentation (accessed Jul. 24, 2022).
  • [18] M. Mason. “Understanding Bayes Error: How a low cost machine learning strategy could have a big impact”. LinkedIn.com. https://www.linkedin.com/pulse/understanding-bayes-error-how-low-cost-machine-learning-malcolm-mason (accessed Jul. 23, 2022).