BFA-YOLO: Balanced multiscale object detection network for multi-view building facade attachments detection
<|reference_start|>BFA-YOLO: Balanced multiscale object detection network for multi-view building facade attachments detection: Detection of building facade attachments such as doors, windows, balconies, air conditioner units, billboards, and glass curtain walls plays a pivotal role in numerous applications. Building facade attachments detection aids in vbuilding information modeling (BIM) construction and meeting Level of Detail 3 (LOD3) standards. Yet, it faces challenges like uneven object distribution, small object detection difficulty, and background interference. To counter these, we propose BFA-YOLO, a model for detecting facade attachments in multi-view images. BFA-YOLO incorporates three novel innovations: the Feature Balanced Spindle Module (FBSM) for addressing uneven distribution, the Target Dynamic Alignment Task Detection Head (TDATH) aimed at improving small object detection, and the Position Memory Enhanced Self-Attention Mechanism (PMESA) to combat background interference, with each component specifically designed to solve its corresponding challenge. Detection efficacy of deep network models deeply depends on the dataset's characteristics. Existing open source datasets related to building facades are limited by their single perspective, small image pool, and incomplete category coverage. We propose a novel method for building facade attachments detection dataset construction and construct the BFA-3D dataset for facade attachments detection. The BFA-3D dataset features multi-view, accurate labels, diverse categories, and detailed classification. BFA-YOLO surpasses YOLOv8 by 1.8% and 2.9% in mAP@0.5 on the multi-view BFA-3D and street-view Facade-WHU datasets, respectively. These results underscore BFA-YOLO's superior performance in detecting facade attachments.<|reference_end|>
@article{chen2024bfa-yolo:,
    title={BFA-YOLO: A balanced multiscale object detection network for building
  fa\c{c}ade attachments detection},
    author={Yangguang Chen, Tong Wang, Guanzhou Chen, Kun Zhu, Xiaoliang Tan,
  Jiaqi Wang, Wenchao Guo, Qing Wang, Xiaolong Luo, Xiaodong Zhang},
    journal={arXiv preprint arXiv:2409.04025},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04025},
    primaryClass={cs.CV cs.AI}
}