title: Ð¡Ð¿Ð¾ÑÐ¾Ð± Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÑ ÐµÐ¼ Ð¸ Ð²ÐµÐ´Ð¾Ð¼Ð¾ÑÑÐµÐ¹ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð·Ð´Ð°Ð½Ð¸Ð¹ Ð¸ ÑÐ¾Ð¾ÑÑÐ¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Method of automation of formation of schemes and lists of defects based on the results of inspection of buildings and structures
Creators: name: ÐÐ¸ÑÐ¸Ð½, ÐÐ»ÐµÐºÑÐ°Ð½Ð´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: 2024
Subjects: Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð·Ð´Ð°Ð½Ð¸Ð¹ Ð¸ ÑÐ¾Ð¾ÑÑÐ¶ÐµÐ½Ð¸Ð¹
ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ðµ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸
Ð²ÐµÐ´Ð¾Ð¼Ð¾ÑÑÐ¸ Ð´ÐµÑÐµÐºÑÐ¾Ð²
ÑÑ ÐµÐ¼Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð²
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð´ÐµÑÐµÐºÑÐ¾Ð²
inspection of buildings and structures
technical condition
automation algorithms
defect lists
defect diagrams
artificial intelligence
deep learning
defect detection

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð½Ð° Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ðµ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸ Ð¾Ð¿Ð¸ÑÑÐ²Ð°ÐµÑ Ð´ÐµÑÐµÐºÑÑ ÑÑÑÐ¾Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÐºÐ¾Ð½ÑÑÑÑÐºÑÐ¸Ð¹ Ð¿Ð¾ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÐ¼ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ°, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ ÑÑÑÑÐºÑÑÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð¸ÑÑÐ¾Ð´Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ, ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ ÑÑÐµÐ¼Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð² ÑÐ¾Ð´Ðµ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ° Ð¸ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸ ÑÐ¾ÑÐ¼Ð¸ÑÑÐµÑ Ð¾ÑÑÐµÑÐ½ÑÑ Ð´Ð¾ÐºÑÐ¼ÐµÐ½ÑÐ°ÑÐ¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¸, Ð·Ð°ÑÑÐ°Ð³Ð¸Ð²Ð°ÑÑÐ¸Ðµ Ð°Ð½Ð°Ð»Ð¾Ð³Ð¸ÑÐ½ÑÐµ Ð¸Ð»Ð¸ ÑÐ¼ÐµÐ¶Ð½ÑÐµ ÑÐµÐ¼Ñ; 2. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ ÑÐ¿Ð¾ÑÐ¾Ð± Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐµÐ¼ Ð¸ Ð²ÐµÐ´Ð¾Ð¼Ð¾ÑÑÐµÐ¹ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ°; 3. ÐÑÐ¾Ð²ÐµÑÐ¸ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ¿Ð¾ÑÐ¾Ð±Ð° Ð¸ Ð¾ÑÐµÐ½Ð¸ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½ Ð¿ÑÐ¾ÑÐµÑÑ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð·Ð´Ð°Ð½Ð¸Ð¹ Ð¸ ÑÐ¾Ð¾ÑÑÐ¶ÐµÐ½Ð¸Ð¹. ÐÑÐ´ÐµÐ»ÐµÐ½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ°. ÐÐ»Ñ Ð¸Ñ ÑÐµÑÐµÐ½Ð¸Ñ ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°, ÐºÐ¾ÑÐ¾ÑÐ°Ñ ÑÐ°Ð·ÑÐ°Ð±Ð°ÑÑÐ²Ð°Ð»Ð°ÑÑ Ð½Ð° ÑÐ·ÑÐºÐ°Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð¸ Ð´ÑÑÐ³Ð¸Ñ. Ð ÐµÑ Ð¾ÑÐ½Ð¾Ð²Ðµ Ð»ÐµÐ¶Ð¸Ñ Ð±Ð°Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ°Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ ÑÑÐ°Ð½Ð¸ÑÑ Ð¸ÑÑÐ¾Ð´Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ. ÐÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð¾ÑÐ²ÐµÑÐ°ÐµÑ Ð·Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÑ. ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð½Ð° Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸, Ð²ÑÐ´ÐµÐ»ÐµÐ½ Ð¼ÐµÑÐ¾Ð´ YOLO, Ð²ÐµÑÑÐ¸Ð¸ YOLOv5, ÐºÐ°Ðº Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ð¹ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ ÑÑÐµÑÑ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ ÑÐ¿Ð¾ÑÐ¾Ð± ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÑÐ°ÑÑÐµÐ¹ Ð²ÑÐ±Ð¾ÑÐºÐ¸ Ð´Ð»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð·Ð´Ð°Ð½Ð¸Ð¹ Ð¸ ÑÐ¾Ð¾ÑÑÐ¶ÐµÐ½Ð¸Ð¹, Ð²ÑÐ´ÐµÐ»ÐµÐ½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ ÐºÑÐ¸ÑÐµÑÐ¸Ð¸ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ°. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÑÐ¾Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÐºÐ¾Ð½ÑÑÑÑÐºÑÐ¸Ð¹ Ð½Ð° Ð¿ÑÐ¸Ð¼ÐµÑÐµ Ð¾Ð±ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð½Ð¾Ð³Ð¾ÐºÐ²Ð°ÑÑÐ¸ÑÐ½Ð¾Ð³Ð¾ Ð¶Ð¸Ð»Ð¾Ð³Ð¾ Ð´Ð¾Ð¼Ð°. ÐÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾ÑÐµÑÑÐ° ÑÐ°Ð±Ð¾Ñ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¸ Ð±ÐµÐ· Ð½ÐµÑ. Ð¡Ð¾Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾, ÑÑÐ¾ Ð¿Ð¾ Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¼Ñ ÑÐ°ÑÑÐµÑÑ ÑÑÐ¾Ð¸Ð¼Ð¾ÑÑÑ ÑÐ°Ð±Ð¾Ñ ÑÐ¼ÐµÐ½ÑÑÐ¸Ð»Ð°ÑÑ Ð±Ð¾Ð»ÐµÐµ ÑÐµÐ¼ Ð½Ð° 30%. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸ÑÑ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° Ð±Ð¾Ð»ÐµÐµ 75%.
description: This work is devoted to the development of automation algorithms and creation of a program based on them, which automatically describes the defects of building structures by photos using an artificial intelligence model, allows structuring the initial data and the results of the inspection, to form parameterized schemes of defects during the inspection of the object and automatically generates reporting documentation. The tasks to be solved in the course of the research: 1. Analyze the sources touching on similar or related topics; 2. To develop a way to automate the formation of schemes and lists of defects using artificial intelligence technologies; 3. To test the work of the method and evaluate the results. The paper considers the process of inspection of buildings and structures. The main problems of this process are highlighted. To solve them, a program is formed, which was developed in the programming languages Python and others. It is based on a database and neural network model. The database allows storing the initial data and the results of the inspection. The neural network is responsible for recognizing defects in photographs. According to the results of analysis of modern methods of formation of neural networks based on deep learning, the method YOLO, version YOLOv5, is selected as the most suitable for solving the problems of the survey sphere. The method of forming a training sample for neural network within the field of inspection of buildings and structures is presented, the main criteria of this process are highlighted. As a result, the developed method of automating the processes of inspection of building structures has been tested on the example of inspection of an apartment building. The comparison of the work process with the application of the developed program and without it was made. The comparison showed that according to the preliminary calculation the cost of works decreased by more than 30%. The accuracy of the neural network model for describing defects in photographs was more than 75%.