AH-YOLO: An Improved YOLOv8-Based Lightweight Model for Fire Detection in Aircraft Hangars

As high-specification structures, civil aircraft hangars face significant fire risks, including rapid fire propagation and challenging rescue operations. The structural integrity of these hangars is compromised under high temperatures, potentially leading to collapse and making aircraft parking and maintenance unfeasible. The severe consequences of fire in such environments make effective detection essential for mitigating risks and enhancing flight safety. However, conventional fire detectors often suffer from false alarms and missed detections, failing to meet the fire safety demands of large buildings. Additionally, many existing fire detection models are computationally intensive and large in size, posing deployment challenges in resource-limited environments. To address these issues, this paper proposes an improved YOLOv8-based lightweight model for fire detection in aircraft hangars (AH-YOLO). A custom infrared fire dataset was collected through controlled burn experiments in a real aircraft hangar, using infrared thermal imaging cameras for their long-range detection, high accuracy, and robustness to lighting conditions. First, the MobileOne module is integrated to reduce the network complexity and improve the computational efficiency. Additionally, the CBAM attention mechanism enhances fine target detection, while the improved Dynamic Head boosts the target perception. The experimental results demonstrate that AH-YOLO achieves 93.8% [email protected] on this custom dataset, a 3.6% improvement over YOLOv8n while reducing parameters by 15.6% and increasing frames per second (FPS) by 19.0%.