Abstract: Based on visual information, fire detection is of great significance to fire protection work. However, most of the methods proposed by relevant research institutions at this stage are based on high-performance hardware devices, which limits the practical deployment and application of relevant results. In response to this, this paper uses ShuffleNetv2 network as the main backbone to construct a lightweight model based on YOLOv5 target detection model, and introduces the SIoU loss function to improve the positioning accuracy of the model's target box. Additionally, the Shuffle Attention module is added to the model to improve its recognition accuracy of flame targets in complex environments. Experiments have shown that compared to the original YOLOv5 model, the improved model not only achieves better recognition results but also reduces the parameter count by 54.2% and improves detection speed by 40.5%. Finally, the model is deployed to embedded devices to verify its application efficiency, and the results show that while maintaining recognition performance, the model achieves a detection speed of 32 f/s.

Key words: convolutional neural networks, fire monitoring, Yolov5, attention module, Jetson Nano