  Change detection (CD) is an essential earth observation technique. It
captures the dynamic information of land objects. With the rise of deep
learning, convolutional neural networks (CNN) have shown great potential in CD.
However, current CNN models introduce backbone architectures that lose detailed
information during learning. Moreover, current CNN models are heavy in
parameters, which prevents their deployment on edge devices such as UAVs. In
this work, we tackle this issue by proposing RDP-Net: a region detail
preserving network for CD. We propose an efficient training strategy that
constructs the training tasks during the warmup period of CNN training and lets
the CNN learn from easy to hard. The training strategy enables CNN to learn
more powerful features with fewer FLOPs and achieve better performance. Next,
we propose an effective edge loss that increases the penalty for errors on
details and improves the network's attention to details such as boundary
regions and small areas. Furthermore, we provide a CNN model with a brand new
backbone that achieves the state-of-the-art empirical performance in CD with
only 1.70M parameters. We hope our RDP-Net would benefit the practical CD
applications on compact devices and could inspire more people to bring change
detection to a new level with the efficient training strategy. The code and
models are publicly available at https://github.com/Chnja/RDPNet.
