Intelligent identification and safety assessment of buildings based on computer vision: a case study of rural villages in Jilin province

Lili Qu

doi:10.1117/12.3037832

12 September 2024 Intelligent identification and safety assessment of buildings based on computer vision: a case study of rural villages in Jilin province

Lili Qu

Author Affiliations +

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325608 (2024) https://doi.org/10.1117/12.3037832
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

Traditional rural building management mostly relies on manual work, which is not only inefficient, but also easy to cause security risks. On this basis, this paper plans to integrate the building information based on computer vision technology into the map data, and realize the overall planning and safety management of the village by combining with real-time data such as meteorology and geology. Through the identification and safety evaluation of rural houses, an intelligent early warning system is constructed to realize early warning and risk disposal of villages and towns, thus improving the level of rural disaster prevention and mitigation and safety management. The F1 values of farmhouses, granaries and public facilities are 91.2%, 87.2% and 91.1% respectively, and the overall F1 value is 89.8%, which shows that the model performs well in balance accuracy and recall. Using computer vision technology, we can realize automatic identification and safety evaluation of massive buildings and improve management efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lili Qu "Intelligent identification and safety assessment of buildings based on computer vision: a case study of rural villages in Jilin province", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325608 (12 September 2024); https://doi.org/10.1117/12.3037832

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KEYWORDS

Buildings

Safety

Computer vision technology

Education and training

Data modeling

Information fusion

Intelligence systems

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