Abstract
Change detection typically involves identifying regions with changes between bitemporal images taken at the same location. Besides significant changes, slow changes in bitemporal images are also important in real-life scenarios. For instance, weak changes often serve as precursors to major hazards in scenarios like slopes, dams, and tailings ponds. Therefore, designing a change detection network that simultaneously detects slow and fast changes presents a novel challenge. In this paper, to address this challenge, we propose a change detection network named Flow-CDNet, consisting of two branches: optical flow branch and binary change detection branch. The first branch utilizes a pyramid structure to extract displacement changes at multiple scales. The second one combines a ResNet-based network with the optical flow branch's output to generate fast change outputs. Subsequently, to supervise and evaluate this new change detection framework, a self-built change detection dataset Flow-Change, a loss function combining binary tversky loss and L2 norm loss, along with a new evaluation metric called FEPE are designed. Quantitative experiments conducted on Flow-Change dataset demonstrated that our approach outperforms the existing methods. Furthermore, ablation experiments verified that the two branches can promote each other to enhance the detection performance.
Abstract (translated)
变化检测通常涉及识别在相同位置拍摄的双时态图像之间的变化区域。除了显著的变化之外，双时态图像中的缓慢变化在实际场景中也很重要。例如，在斜坡、大坝和尾矿库等情况下，微弱的变化往往预示着重大危险的到来。因此，设计一种能够同时检测缓慢变化和快速变化的变体检测网络提出了一个新的挑战。在这篇论文中，为了应对这一挑战，我们提出了一种名为Flow-CDNet的变化检测网络，该网络包括两个分支：光流分支和二进制变化检测分支。第一个分支利用金字塔结构在多个尺度上提取位移变化。第二个分支结合基于ResNet的网络与光流分支的输出以生成快速变化的结果。随后，为了监督和评估这一新的变化检测框架，我们构建了一个自建的变化检测数据集Flow-Change，并设计了一种结合二进制Tversky损失和L2范数损失的损失函数，以及一个新的评价指标FEPE（未详细定义）。在Flow-Change数据集上进行的定量实验表明，我们的方法优于现有方法。此外，消融实验验证了这两个分支可以相互促进以增强检测性能。
URL
https://arxiv.org/abs/2507.02307
