BFDD: A Pixel-Level Aligned RGB-IR Image Dataset for Building Façade Defect Segmentation

The Building Façade Defect Detection (BFDD) Dataset is a novel multimodal dataset specifically designed to semantic segmentation research in structural health monitoring and automated building inspection. This dataset comprises 788 strictly aligned pairs of visible (RGB) and infrared (IR) images covering 5 common façade defect categories: Cracks, Peeling, Hollow Areas, Stains, and Erosion. Each of the image pairs have been cropped to the same size: 640×512 pixels. The visible imagery captures rich surface textures and color variations, while the thermal modality reveals sub-surface delamination (hollows) and moisture-related anomalies often invisible in the RGB spectrum. Since the raw data consist of image sequences acquired during inspection runs, where frames are captured in rapid succession along similar trajectories, in dataset partitioning, a SfM aware grouping strategy was employed (for details, please refer to the article). The image pairs were aligned using a robust feature-based registration method. This ensures strict pixel-level correspondence between the RGB textures and the IR thermal signatures, enabling rigorous training and evaluation of multimodal fusion networks. Each image in the dataset has been manually annotated in pixel level. The dataset is publicly available to foster reproducible research in multimodal computer vision for civil infrastructure.

搜集汇总

背景与挑战

背景概述

BFDD数据集是一个专门用于建筑立面缺陷分割的多模态数据集，包含788对严格对齐的可见光（RGB）和红外（IR）图像，覆盖裂缝、剥落、空鼓、污渍和侵蚀五种常见缺陷，每对图像尺寸为640×512像素，并提供像素级人工标注，以支持多模态融合网络的训练和评估。