Multi-Stage Group Interaction and Cross-Domain Fusion Network for Real-Time Smoke Segmentation

Lightweight smoke image segmentation is essential for fire warning systems, particularly on mobile devices. In recent years, although numerous high-precision, large-scale smoke segmentation models have been developed, there are few lightweight solutions specifically designed for mobile applications. Therefore, we propose a Multi-stage Group Interaction and Cross-domain Fusion Network (MGICFN) with low computational complexity for real-time smoke segmentation. To improve the model's ability to effectively analyze smoke features, we incorporate a Cross-domain Interaction Attention Module (CIAM) to merge spatial and frequency domain features for creating a lightweight smoke encoder. To alleviate the loss of critical information from small smoke objects during downsampling, we design a Multi-stage Group Interaction Module (MGIM). The MGIM calibrates the information discrepancies between high and low-dimensional features. To enhance the boundary information of smoke targets, we introduce an Edge Enhancement Module (EEM), which utilizes predicted target boundaries as advanced guidance to refine lower-level smoke features. Furthermore, we implement a Group Convolutional Block Attention Module (GCBAM) and a Group Fusion Module (GFM) to connect the encoder and decoder efficiently. Experimental results demonstrate that MGICFN achieves an 88.70% Dice coefficient (Dice), an 81.16% mean Intersection over Union (mIoU), and a 91.93% accuracy (Acc) on the SFS3K dataset. It also achieves an 87.30% Dice, a 78.68% mIoU, and a 92.95% Acc on the SYN70K test dataset. Our MGICFN model has 0.73M parameters and requires 0.3G FLOPs.