Fire detection methods based on an optimized YOLOv5 algorithm

Abstract

Computer vision technology has broad application prospects in the field of intelligent fire detection, which has the benefits of accuracy, timeliness, visibility, adjustability, and multi-scene adaptability. Traditional computer vision algorithm flaws include erroneous detection, detection gaps, poor precision, and slow detection speed. In this paper, the efficient and lightweight YOLOv5s model is used to detect fire flames and smoke. The attention mechanism is embedded into the C3 module to enhance the backbone network and maximize the algorithm's suppression of invalid feature data. Alpha CIOU is adopted to improve the positioning function and detection target. At the same time, the concept of transfer learning is used to realize semi-automatic data annotation, which reduces training expenses in terms of manpower and time. The comparative experiments of six distinct fire detection algorithms (YOLOv5 and five optimization algorithms) are carried out. The results indicate that the self-attention mechanism based on the Transformer structure has a substantial impact on enhancing target detection precision. The improved location function based on Alpha CIOU aids in enhancing the detection recall rate. The average recall rate of fire detection of the YOlOv5+TR+αCIOU algorithm is the highest, which is 68.5%, clearly outperforming other algorithms. Based on the surveillance video, this optimization algorithm is utilized to detect a fire in a factory, and the fire is detected in the 9th second when it starts to appear. The results demonstrate the algorithm's viability for real-time fire detection.