BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection

• URL: http://arxiv.org/abs/2409.04025v2
• Date: Mon, 11 Nov 2024 06:23:21 GMT
• Title: BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection
• Authors: Yangguang Chen, Tong Wang, Guanzhou Chen, Kun Zhu, Xiaoliang Tan, Jiaqi Wang, Wenchao Guo, Qing Wang, Xiaolong Luo, Xiaodong Zhang,
• Abstract summary: We develop 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 faccade attachments. BFA-YOLO demonstrated improvements of 1.8% and 2.9% in mAP$_50$ on the BFA-3D dataset and the public Facade-WHU dataset.
• Score: 24.100350794625882
• License:
• Abstract: The detection of fa\c{c}ade 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). Yet, this field faces significant challenges, including the uneven distribution of fa\c{c}ade elements, the presence of small objects, and substantial background noise, which hamper detection accuracy. To address these issues, we develop 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 fa\c{c}ade attachments. 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 fa\c{c}ade attachment categories. This dataset is developed to address the limitations present in existing fa\c{c}ade 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 mAP$_{50}$ on the BFA-3D dataset and the public Fa\c{c}ade-WHU dataset, respectively, when compared to the baseline YOLOv8 model. These results highlight the superior performance of BFA-YOLO in fa\c{c}ade element detection and the advancement of intelligent BIM technologies.

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