用深度学习自动识别2D平面图中的照明设备，助力高效建筑设计与能耗估算。
Intelligent Detection of Mechanical, Electrical, and Plumbing (MEP) Metrics Based on 2D Floor Plans
- 基于Mask RCNN构建模型，从平面图中检测灯光符号及文本信息
- 检测框和分割的mAP分别达0.7596和0.7111，高IoU下表现更优
- 适用于建筑、施工行业，推动节能建筑设计自动化
本研究开发了一种基于神经网络的模型，用于从2D平面图中提取各类信息。该模型可检测照明符号、识别灯具类型，并提取相关文字信息。研究旨在实现高效楼层设计，确定每层所需的灯具数量与类型，进而估算电力需求。模型以Mask RCNN为基础，图像经标注后转为Coco数据格式用于训练。测试结果显示，边界框mAP为0.7596，分割mAP为0.7111；在不同IoU阈值下表现良好，其中bbox_mAP 50为0.9850，segm_mAP 75为0.9219。该模型有助于建筑与施工等行业提升设计效率，建立自动化工作流，是实现节能建筑设计工具的第一步。
原文摘要 · Abstract (English)
This research developed a neural network-based model to extract various information from 2D floor plans. We detect lighting symbols, identify the appropriate type of light, and extract the associated texts with lights. The study aims to enable efficient floor designing and determining the number and type of lights needed per floor, i.e., allow efficient design and estimate the power requirement of the floor plan. The model was developed using Mask RCNN as the base. The images were annotated and converted into a Coco data format for training the model. The model achieved bbox\_mAP and segm\_mAP values of 0.7596 and 0.7111, respectively. It also performed well at different IoU thresholds, i.e., with bbox\_mAP 50 and segm\_mAP 75 values of 0.9850 and 0.9219, respectively. The developed model will help various industries, such as architecture and construction, to improve design time and create efficient workflows by automatically detecting Mechanical, Electrical, and Plumbing (MEP) objects from floor plans, and it is the first step towards building tools that will help energy-efficient building design.
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