Talk · International Conference on Modeling, Simulation & Intelligent Computing · December 10, 2025

Fine-Tuning Optimization Approaches for Enhanced Real-Time Fire Detection Using YOLO Architecture

Bekbol Sultankulov, Marat Nurtas, Yerbol Sultankulov, Nurgul Abenova

Abstract

Early fire detection is essential for safeguarding lives and infrastructure. While deep learning-based object detection has shown promise, achieving both high accuracy and real-time performance in fire detection remains challenging. This study proposes an enhanced fire detection approach utilizing the YOLOv9 architecture with optimized fine-tuning techniques. The methodology improves detection accuracy while maintaining real-time processing through systematic model optimization. The key contributions include: (1) development of a comprehensive fire detection dataset covering diverse scenarios, (2) implementation of an effective fine-tuning strategy that preserves essential pretrained features, and (3) optimization of model parameters for fire-specific detection tasks. Transfer learning with carefully tuned hyperparameters is employed to enhance fire event discrimination under various environmental conditions. Experimental results demonstrate that the fine-tuned YOLOv9 model outperforms the baseline in detection accuracy while satisfying real-time processing constraints. The proposed approach offers a practical solution for robust fire detection systems, particularly suitable for deployment in buildings and industrial environments where rapid response is critical.

Fire Detection and Safety SystemsAdvanced Neural Network ApplicationsFire dynamics and safety researchfire detectionYOLOv9fine-tuning

Fine-Tuning Optimization Approaches for Enhanced Real-Time Fire Detection Using YOLO Architecture

Abstract

More by Bekbol Sultankulov