The high computational complexity and large model scales of existing cloud detection algorithms render their deployment on edge devices almost infeasible. To address this challenge, this study proposed a MobileNet-based lightweight cloud detection model. In the downsampling stage, a residual module based on the attention mechanism was employed to reduce model parameters through group convolution. The channel shuffling mechanism and the squeeze-and-excitation (SE) channel attention were integrated to enhance the information exchange between channels. These approaches reduced parameters and computational complexity while maintaining the ability to extract significant features. In the upsampling stage, the RepConv module and the improved atrous spatial pyramid pooling (ASPP) module were used to enhance the network’s learning capability and its ability to capture image details and spatial information. Experimental results demonstrate that the proposed model can achieve higher cloud detection accuracy while reducing parameters and model complexity, substantiating its practicality and feasibility.