Semantic Recognition of Doors and Windows in Architectural Facade Images Based on Mask R-CNN
Parsing building facades is crucial for tasks such as urban analysis,semantic reconstruction,and energy demand assessment,which require high-quality semantic data.However,traditional manual measurement and modeling methods are often time-consuming.This article explores a method based on transfer learning with Mask R-CNN networks for automatic extraction of door and window coordinates in building facade images.The dataset selected for this study is the CMP dataset,which is currently the benchmark dataset with the highest number of annotated images,and it was adapted to train the network by converting the data format.The results demonstrate that this method can achieve formatted output of door and window corner coordinates in building facade images with high precision.Through this work,we aim to provide a solution approach for research in the field of high-detail architectural 3D modeling,such as oblique photography.
three-dimensional modeling of buildingsfacade parsingsemantic segmentationtransfer learning
NETL
NSTL
万方数据
