LightCDNet: Lightweight Change Detection Network Based on VHR Images

Lightweight change detection models are essential for industrial applications and edge devices. Reducing the model size while maintaining high accuracy is a key challenge in developing lightweight change detection models. However, many existing methods oversimplify the model architecture, leading to a loss of information and reduced performance. Therefore, developing a lightweight model that can effectively preserve the input information is a challenging problem. To address this challenge, we propose LightCDNet, a novel lightweight change detection model that effectively preserves the input information. LightCDNet consists of an early fusion backbone network and a pyramid decoder for end-to-end change detection. The core component of LightCDNet is the Deep Supervised Fusion Module (DSFM), which guides the early fusion of primary features to improve performance. We evaluated LightCDNet on the LEVIR-CD dataset and found that it achieved comparable or better performance than state-of-the-art models while being 10-117 times smaller in size.

Automated summary

Lightweight change detection models are important for industrial applications and edge devices. Developing such models with high accuracy and reduced size is a challenge. Existing methods oversimplify the model architecture, resulting in loss of information and reduced performance. To address this, we propose LightCDNet, a lightweight change detection model that effectively preserves input information. Evaluation on the LEVIR-CD dataset shows that LightCDNet achieves comparable or better performance while being much smaller in size compared to state-of-the-art models.