Open Access

ARTICLE

Enhancing Fire Detection Performance Based on Fine-Tuned YOLOv10

Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu

Faculty of Information Technology 2, Posts and Telecommunications Institute of Technology (PTIT), Ho Chi Minh City, 700000, Vietnam

* Corresponding Author: Trong Thua Huynh. Email:

Computers, Materials & Continua 2024, 81(2), 2281-2298. https://doi.org/10.32604/cmc.2024.057954

Received 31 August 2024; Accepted 17 October 2024; Issue published 18 November 2024

Abstract

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.

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