An increasing number of change detection models are designed with different convolutional neural network (CNNs). However, the mechanism for designing network layers that can effectively extract robust features for different scenes remains unclear. Thus, novel networks with fluid pyramid integration network (FPIN) to detect changes are proposed in this study. Specifically, we first extract multi-scale deep features from feature extraction network. Higher layers extract semantic features robust to illumination and camera pose variations, while lower layers extract texture features that generate clear boundaries and changes with small scales. To employ the advantages of the features extracted from different layers, FPIN progressively fuses higher and lower layer features in a hierarchical order. We propose three different change detection networks based on FPIN and validate them on three publicly available change detection benchmark datasets. Experimental results showed that the proposed networks are better than state-of-the-art change detection methods.

使用不同的卷积神经网络（CNN）设计了越来越多的变化检测模型。但是，用于设计网络层以有效地提取不同场景的鲁棒特征的机制仍然不清楚。因此，本研究提出了具有流动金字塔集成网络（FPIN）来检测变化的新型网络。具体来说，我们首先从特征提取网络中提取多尺度深度特征。较高的层提取对照明和相机姿势变化具有鲁棒性的语义特征，而较低的层提取生成清晰边界和小比例变化的纹理特征。为了利用从不同层提取的特征的优点，FPIN逐步将高层和较低层的特征按层次结构融合在一起。我们建议基于FPIN的三个不同的变更检测网络，并在三个公开可用的变更检测基准数据集上对其进行验证。实验结果表明，提出的网络优于最新的变化检测方法。