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逐步主动学习的力量在地图图像中用于能源评估
Dhoyazan Al-Turki1, Marios Kyriakou1, Shadi Basurra1
1School of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7BD, UK.
Scientific reports
|September 27, 2023
概括
本研究引入了一个主动学习模型,用于自动化能源评估的地图分析,大大减少了手动注释时间. 人工智能模型有效地检测了关键的地图元素,有助于节能建筑改造规划.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 楼层规划的能源评估提供了高效的,非物理评估住宅的能源性能.
- 准确的建筑热损耗/热收益确定有助于规划节能改造.
- 人工智能模型培训的地图手动注释是耗时且具有挑战性的.
研究的目的:
- 开发一种新的主动学习模型,用于检测和注释地图图像中的主要元素.
- 为能源评估人员自动化地板图的分析,解决手动注释的瓶问题.
- 提高创建基于地图的能源评估人工智能模型的效率.
主要方法:
- 采用了积极的学习方法,从500张注释图片开始.
- 该模型从另外4500张未标记的地图图像中代地学习.
- 该模型专注于在地图中检测和注释关键元素.
主要成果:
- 积极学习模型实现了0.833.3的平均平均精度得分.
- 该模型表现出高性能,精度得分为0.972和回忆得分为0.950.
- 开发的地图数据集已公开提供.
结论:
- 拟议的积极学习模型有效地自动化了用于能源评估的地图元素检测.
- 这种方法显著减少了人工智能模型开发在这个领域所需的手工工作.
- 公开可用的数据集支持进一步研究用于用于建筑能效的自动化地图分析.
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