Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background-changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.

翻译：变化检测（CD）旨在从同一场景长时间跨度捕获的两幅图像中，将物体变化（即物体缺失或出现）与背景变化（即环境变化，如光照和季节变化）分离开来，在灾害管理、城市发展等领域具有关键应用。特别是，背景变化的无尽模式要求检测器对未见过的环境变化具有高泛化能力，这使得该任务极具挑战性。近年来基于深度学习的方法通过配对训练样本开发新型网络架构或优化策略，但未能显式处理泛化问题，且需要大量人工像素级标注工作。本研究首次在变化检测领域从数据增强角度研究泛化问题，开发了一种仅需图像级标签的新型弱监督训练算法。与通用的分类数据增强技术不同，我们提出了专为变化检测设计的背景混合增强方法，通过一组背景变化图像引导增强样本，使深度变化检测模型能观察到多样的环境变化。此外，我们提出了增强与真实数据一致性损失，显著促进了泛化能力的提升。该方法作为通用框架可增强现有各类基于深度学习的检测器。我们在两个公开数据集上进行了大量实验，并增强了四种最先进方法，验证了本方法的优势。相关代码已开源至 https://github.com/tsingqguo/bgmix。