基于人工智能算法的地震风险调查房屋图识别系统

Zhanling Fu, Chunli Zhang, Wuping Gao, Chengguo Yan
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引用次数: 0

摘要

针对地震风险调查中存在的主要问题,本研究旨在研究、设计和构建一个用于地震风险调查的房屋图纸识别系统。本文以光学字符识别技术(OCR)为核心,结合基于可微分二值化(DBNet)算法的文本检测、基于CRNN+CTC算法的图像识别、基于K-means算法的图像分割等人工智能算法,详细阐述了该系统的运行过程、技术选择、系统设计和应用效果。该系统在人口普查数据的收集、校准和输出等方面发挥了积极作用,提高了地震风险调查的效率和质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An artificial intelligence algorithm-based house drawing recognition system for earthquake risk survey
Considering the major problems in the earthquake risk survey, the study aims at researching, designing, and building a house drawing recognition system for the earthquake risk survey. Focusing on Optical Character Recognition technology (OCR), together with artificial intelligence algorithms such as text detection based on Differentiable Binarization (DBNet) algorithm, CRNN+CTC algorithm-based image recognition, and image segmentation with K-means algorithm, this paper elaborates on the operation process, technology selection, system design, and application effect of the system. The system shows positive effects on the collection, calibration, and output of census data, which improves the efficiency and quality of earthquake risk surveys.
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