title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¸ Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð»Ð°Ð½Ð¾Ð² ÐºÐ²Ð°ÑÑÐ¸Ñ ÐÐ¢Ð Ð¿Ð¾ ÑÐ°ÑÑÑÐ¾Ð²Ð¾Ð¼Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Apartment plan recognition and vectorization by raster image
Creators: name: Ð¡ÑÑÐ¸ÐºÐ¾Ð², ÐÐ»ÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
python
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
image processing
neural networks
computer vision

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description: This work introduces the approach based on the combination of statistical image processing methods in a row of machine learning techniques that allow training robust model for the different floor plan topologies. The research set the following goals: 1. Studying of the relevance of the problem of vectorization of plans in the modern market. 2. Studying of existing approaches to vectorization of apartment plans, identifying their shortcomings. 3. The choice of architecture and teaching models of deep machine learning to recognize walls, windows, doors and other objects. 4. Development of an image processing pipeline that accepts an image as an input and outputs a vector plan format. All data for model training was collected from real estate sites. The developed method of data augmentation allowed to increase learning metrics, as well as to train models that are resistant to shadows in the image. The work presents the results of training. Comparison of various architectures of neural networks for this task is presented. As a result, an algorithm was developed that could vectorize the apartment plan according to a raster image. The algorithm can be used to process various types of apartment plans. The method proposed in this work can be useful for three-dimensional reconstruction, calculating the value of real estate, design proposals, etc.