GLAFC-YOLO: Multimodal Object Detection of Personnel for Indoor Fire Rescue in Smoke-Obscured Environments

Reliable detection of personnel is critical for situational awareness and life-saving interventions during indoor fire rescue operations, where dense smoke rapidly obscures visibility and compromises conventional vision systems. Visible-light cameras fail under such conditions due to severe Mie scattering, while thermal infrared (TIR) imaging—though capable of penetrating smoke—often lacks the fine-grained texture needed to distinguish human forms from background clutter. Furthermore, practical deployment of multimodal sensors is hindered by spatial misalignment between modalities, which degrades fusion efficacy and detection accuracy. To address these challenges, this paper proposes GLAFC-YOLO (Global-Local Alignment and Frequency-aware Cross-attention Fusion), a dual-stream multimodal detection framework specifically designed for personnel localization in smoke-obscured indoor fires. GLAFC-YOLO fuses near-infrared (NIR) and TIR imagery through three novel components: (1) a Global-Local Feature Alignment Subnet (GL-FAS) that rectifies geometric misalignment across modalities; (2) a Modality-Adaptive Frequency Channel Attention (MA-FCA) module that enhances complementary smoke-penetrating thermal signatures and NIR texture cues in the frequency domain; and (3) a Confidence-Aware Transposed Cross-Attention (CA-TCA) mechanism that suppresses smoke-induced artifacts and restores weakened human-centric features. Evaluated on a newly collected multimodal dataset of indoor fire scenarios with annotated personnel, GLAFC-YOLO achieves substantial improvements over the baseline YOLOv11 architecture. Specifically, it achieves Recall improvements of 43.2% and 0.5% compared to unimodal NIR and TIR baselines, respectively. In addition, it achieves improvements of 37.4% and 3.9% in mAP50 and 17.3% and 17.0% in mAP50–95. Experimental results indicate that GLAFC-YOLO outperforms competitive models and reduces personnel miss rates, demonstrating its robustness and readiness for real-world fire-rescue assistance.