Affiliations: [a] School of Information Science and Technology, Qingdao University of Science and Technology, Shandong, China
| [b] School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Shandong, China
Correspondence:
[*]
Corresponding author. Qing Yang, School of Information Science and Technology, Qingdao University of Science and Technology, Shandong, China. E-mail: 03390@qust.edu.cn.
Abstract: Flame and smoke detection is a critical issue that has been widely used in various unmanned security monitoring scenarios. However, existing flame smoke detection methods suffer from low accuracy and slow speed, and these problems reduce the efficiency of real-time detection. To solve the above problems, we propose an improved YOLOv7(You Only Look Once) algorithm for flame smoke mobile detection. The algorithm uses the Kmeans algorithm to cluster the prior frames in the dataset and uses a lightweight CNeB(ConvNext Block) module to replace part of the traditional ELAN module to accelerate the detection speed while ensuring high accuracy. In addition, we propose an improved CIoU loss function to further enhance the detection effect. The experimental results show that, compared with the original algorithm, our algorithm improves the accuracy by 4.5% and the speed by 39.87%. This indicates that our algorithm meets the real-time monitoring requirements and can be practically applied to field detection on mobile edge computing devices.
Keywords: YOLO, fire detect, smoke detect, NVIDIA Jetson
