Cascaded deep learning for flame detection and heat release rate quantification in fire safety

Accurate real-time heat release rate (HRR) measurement is critical in fire safety engineering. This study proposes a cascaded deep learning framework integrating visual flame detection and thermodynamic analysis. The detection module uses an enhanced YOLOv8n with efficient channel attention (ECA) and bidirectional feature pyramid network (BiFPN), achieving 95.2% precision and 88.3% recall. For HRR quantification, a parameter-efficient dual-branch CNN (5.2 M) processes spatial and frequency-domain flame features, showing superior performance (R² = 0.976)-58.5%/78.2% fewer parameters than VGG16/ResNet50 and lower MAE than Vision Transformers (32.47 vs. 41.98). Validated on the NIST fire database, the framework demonstrates robust performance across flame growth and decay phases, with deviations during peak HRR. The cascaded design ensures efficiency by activating HRR analysis only post-detection, which reduces false alarms to a certain extent and establishing a new non-contact fire risk assessment paradigm.