BFA-YOLO: A balanced multiscale object detection network for building façade elements detection
发布时间：2025-05-29
点击次数：
DOI码：10.1016/j.aei.2025.103289
发表刊物：Advanced Engineering Informatics
摘要：The detection of façade elements on buildings, such as doors, windows, balconies, air conditioning units, billboards, and glass curtain walls, is a critical step in automating the creation of Building Information Modeling (BIM). However, this field faces significant challenges, including the uneven distribution of façade elements, the presence of small objects, and substantial background noise, which hamper detection accuracy. To address these issues, we developed the BFA-YOLO model and the BFA-3D dataset in this study. The BFA-YOLO model is an advanced architecture designed specifically for analyzing multi-view images of façade elements. It integrates three novel components: the Feature Balanced Spindle Module (FBSM) that tackles the issue of uneven object distribution; the Target Dynamic Alignment Task Detection Head (TDATH) that enhances the detection of small objects; and the Position Memory Enhanced Self-Attention Mechanism (PMESA), aimed at reducing the impact of background noise. These elements collectively enable BFA-YOLO to effectively address each challenge, thereby improving model robustness and detection precision. The BFA-3D dataset offers multi-view images with precise annotations across a wide range of façade element categories. This dataset is developed to address the limitations present in existing façade detection datasets, which often feature a single perspective and insufficient category coverage. Through comparative analysis, BFA-YOLO demonstrated improvements of 1.8% and 2.9% in mAP50 on the BFA-3D dataset and the public Façade-WHU dataset, respectively, when compared to the baseline YOLOv8 model. These results highlight the superior performance of BFA-YOLO in façade element detection and the advancement of intelligent BIM technologies. The dataset and code are available at https://github.com/CVEO/BFA-YOLO.
合写作者：Kun Zhu,Xiaoliang Tan,Jiaqi Wang,Wenchao Guo,Qing Wang,Xiaolong Luo
论文类型：期刊论文
通讯作者：陈关州,Xiaodong Zhang
文献类型：J
卷号：65
页面范围：103289
是否译文：否
发表时间：2025-05-01
收录刊物：SCI、EI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S147403462500182X