LRCDNET: A LIGHTWEIGHT AND REAL-TIME CLOUD DETECTION NETWORK FOR REMOTE SENSING IMAGES

Most CNN-based cloud detection methods have high computational complexity, large parameter size, and slow inference speed, which limit their practical applications. Furthermore, cloud detection is a challenging task due to the irregular shapes and random sizes of clouds, often leading to inaccurate detection. To overcome these challenges, we propose a lightweight and real-time network (LRCDNet) tailored for cloud detection. By incorporating Short-Term Dense Concatenate (STDC) module, Multi-group Deformable Convolution (DCNv3) and multiple linear self-attention mechanisms, LRCDNet can effectively extract detail information and adaptively build long-term dependencies. In comparison to the state-of-the-art cloud detection and typical real-time semantic segmentation methods, our proposed LRCDNet strikes a better balance between accuracy and computational costs. Specifically, when tested on the GF-1 WHU dataset, LRCDNet achieves an overall accuracy (OA) of 97.37%, a F1-score of 92.42%, and a remarkable inference speed of 122.83 Frames Per Second (FPS) on a RTX A4000 GPU, with only 19.72MB parameters and 10.69G FLOPs calculations.