A Lightweight Real-time Fire Detection Framework for IoT Devices Utilizing Fine-tuned YOLOv10 and Accelerator Module

Abstract

This paper presents a novel real-time fire detection framework tailored for IoT devices by integrating the fine-tuned YOLOv10 model with the Accelerator module. Trained on the FireSmokeDataset (Roboflow) and an additional dataset we collected via Roboflow, the system covers fire, smoke, and distracting objects. Optimized for resource-constrained edge devices, the framework demonstrates exceptional performance, achieving high mean average precision (mAP) for fire and smoke detection, with metrics exceeding 84% and a maximum mAP50 of over 91%. We target deployments in residential homes, industrial facilities, and forest monitoring stations. A key contribution of the proposed framework is the construction of a diverse dataset encompassing fire, smoke, and distracting objects - an element often overlooked in existing fire detection datasets. Additionally, fine-tuning the YOLOv10 model components in conjunction with hardware acceleration ensures both prediction accuracy and improved inference response performance. Comprehensive evaluations confirm the system's robustness, scalability, and practicality under various operating conditions. Through experimental analysis, the YOLOv10-S (small) model stands out for its balance between efficiency and resource usage, making it a suitable choice for low-cost real-time applications with resource constraints. By utilizing the Coral Accelerator, the proposed framework reduces inference time by 58% compared to CPU-based implementations, achieving a latency of just 1.7 seconds per frame. The system's lightweight design ensures reliable deployment in remote areas with limited computational resources and unstable network connectivity, maintaining high accuracy while minimizing false alarms.