Enhancing Fire Detection Performance Based on Fine-Tuned YOLOv10  

作　　者：Trong Thua Huynh Hoang Thanh Nguyen Du Thang Phu 

机构地区：[1]Faculty of Information Technology 2,Posts and Telecommunications Institute of Technology(PTIT),Ho Chi Minh City,700000,Vietnam

出　　处：《Computers, Materials & Continua》2024年第11期2281-2298,共18页计算机、材料和连续体（英文）

摘　　要：In recent years,early detection and warning of fires have posed a significant challenge to environmental protection and human safety.Deep learning models such as Faster R-CNN(Faster Region based Convolutional Neural Network),YOLO(You Only Look Once),and their variants have demonstrated superiority in quickly detecting objects from images and videos,creating new opportunities to enhance automatic and efficient fire detection.The YOLO model,especially newer versions like YOLOv10,stands out for its fast processing capability,making it suitable for low-latency applications.However,when applied to real-world datasets,the accuracy of fire prediction is still not high.This study improves the accuracy of YOLOv10 for real-time applications through model fine-tuning techniques and data augmentation.The core work of the research involves creating a diverse fire image dataset specifically suited for fire detection applications in buildings and factories,freezing the initial layers of the model to retain general features learned from the dataset by applying the Squeeze and Excitation attention mechanism and employing the Stochastic Gradient Descent(SGD)with a momentum optimization algorithm to enhance accuracy while ensuring real-time fire detection.Experimental results demonstrate the effectiveness of the proposed fire prediction approach,where the YOLOv10 small model exhibits the best balance compared to other YOLO family models such as nano,medium,and balanced.Additionally,the study provides an experimental evaluation to highlight the effectiveness of model fine-tuning compared to the YOLOv10 baseline,YOLOv8 and Faster R-CNN based on two criteria:accuracy and prediction time.

关 键 词：Fire detection ACCURACY prediction time fine-tuning real-time YOLOv10 Faster R-CNN 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]