A Semantic Segmentation Method for Flame and Water Stream Landing Points Based on Dual-CBAM-MobileNet

Aiming at the problems of insufficiently rapid fire identification and imprecise fire suppression in large-space fire protection systems, this study designs a Dual-CBAM-MobileNet (dual attention-driven feature extraction network) module based on the encoder-decoder architecture of DeepLabV3+ to achieve real-time and accurate segmentation of flames and water stream landing points. Firstly, a lightweight MobileNetV2 feature extraction network is adopted as the backbone. Secondly, the Convolutional Block Attention Module (CBAM) is introduced and embedded into the feature extraction network to enhance the perception capability for flames and water stream landing points. Finally, to further mitigate the class imbalance problem, Focal Loss is employed as the loss function. Experiments demonstrate that the proposed algorithm improves the image segmentation accuracy for both flames and water stream landing points while maintaining a high image processing speed. On the self-constructed flame-water stream landing point dataset, it achieves a mean Intersection over Union (mIoU) of 76.23% and a processing speed of 78.68 frames per second (FPS), meeting the dual requirements for real-time performance and accuracy in practical fire protection systems.