Fire incidents cause devastating environmental damage and human casualties, necessitating robust automated detection systems. Existing fire recognition methods struggle with visual ambiguities, illumination variations, and computational constraints, while current attention mechanisms lack hierarchical integration for comprehensive feature refinement. We propose a cascaded multi-attention architecture that combines Multi-Scale Strip Attention (MSSA), Optimized Spatial Attention (OSA), and the Convolutional Block Attention Module (CBAM) to enhance fire detection. MSSA employs three-scale orthogonal strip pooling to capture fire patterns across varying spatial extents through horizontal and ver... More >