Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention

Fires pose significant threats to human safety, health, and property. Traditional methods, with their inefficient use of features, struggle to meet the demands of fire detection. You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate and identify fire and smoke objects in visual images. However, research utilizing the latest YOLO11 for fire and smoke detection remains sparse, and addressing the scale variability of fire and smoke objects as well as the practicality of detection models continues to be a research focus. This study first compares YOLO11 with classic models in the YOLO series to analyze its advantages in fire and smoke detection tasks. Then, to tackle the challenges of scale variability and model practicality, we propose a Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into YOLO11 to create YOLO11s-MSCA. Experimental results show that YOLO11 outperforms other YOLO models by balancing accuracy, speed, and practicality. The YOLO11s-MSCA model performs exceptionally well on the D-Fire dataset, improving overall detection accuracy by 2.6% and smoke recognition accuracy by 2.8%. The model demonstrates a stronger ability to identify small fire and smoke objects. Although challenges remain in handling occluded targets and complex backgrounds, the model exhibits strong robustness and generalization capabilities, maintaining efficient detection performance in complicated environments.