SAMS-Net: A Stage-Decoupled Semantic Segmentation Network for Forest Fire Detection

High-precision and real-time monitoring of forest fires is a critical requirement in disaster prevention and mitigation. During fire evolution, significant stage-wise variations occur, which make it difficult for conventional semantic segmentation models to simultaneously achieve robust multi-scale feature extraction and strong interference resistance. To address this issue, this paper proposes a stage-aware multi-head segmentation network, termed SAMS-Net. The proposed network decouples fire-stage recognition from pixel-level segmentation and employs a Hard-Switch Routing mechanism to dynamically activate the stage-specific decoder that matches the current fire phase during inference, while pruning irrelevant branches to reduce computational redundancy. Experimental results show that SAMS-Net achieves 76.16% mIoU, 81.30% Dice, and 90.31% PA, outperforming mainstream segmentation models such as FCN, U-Net++, DeepLabV3, and YOLOv9-Seg. In challenging stages, particularly the early and recession phases, the segmentation performance improves by more than 10% compared with the second-best model. Meanwhile, the proposed method maintains high accuracy with a real-time inference speed of 75.8 FPS. These results support the effectiveness of SAMS-Net for flame-and-ember foreground segmentation on the constructed multi-stage forest-fire benchmark dataset. Broader generalization across independent datasets and real-world deployment scenarios will be further investigated in future work.