发表机构
Wilfrid Laurier University (WLU)（劳里埃大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以Mask RCNN为基础开发神经网络模型，从二维平面图中检测MEP相关信息，在多个精度指标上表现良好，可助力建筑行业提升设计效率，是节能建筑设计工具开发的重要一步。
AI 中文摘要
本研究开发了一种基于神经网络的模型，用于从二维平面图中提取各类信息，具体包括检测照明符号、识别对应的灯具类型，并提取与灯具相关的文本内容。该研究旨在实现高效的平面图设计，确定每层所需灯具的数量和类型，即支持高效设计并估算平面图的电力需求。模型以Mask RCNN为基础开发，对图像进行标注后转换为Coco数据格式以训练模型。该模型的边界框平均精度（bbox_mAP）为0.7596，分割平均精度（segm_mAP）为0.7111；在不同交并比（IoU）阈值下表现良好，其中bbox_mAP@50为0.9850，segm_mAP@75为0.9219。所开发的模型将助力建筑、施工等各行业，通过自动从平面图中检测机电（MEP）对象，缩短设计时间并构建高效工作流程，是开发助力节能建筑设计工具的第一步。
英文摘要
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.
