A Multi-Camera Edge AI System for Real-Time Smoke and Fire Recognition Using Lightweight Deep Learning Models

Abstract

Vision-based monitoring systems are increasingly adopted in indoor environments to enhance fire safety by leveraging existing surveillance infrastructure. However, practical deployment remains challenging due to the computational demands of deep learning models and the need to process multiple camera streams in real time on resource-constrained devices. This paper presents a multi-camera edge AI system for real-time smoke and fire recognition using lightweight deep learning models. The proposed system operates on a Raspberry Pi 5 and integrates seamlessly with standard IP cameras, enabling deployment without additional sensing hardware. A MobileNetV2-based classification model is employed for efficient frame-level recognition, while a YOLO-based detection model is used for comparative analysis. A latency-aware processing pipeline is further designed to support multiple camera streams under limited computational resources. Experimental results show that the proposed system achieving an F1-score of 0.925 while reducing inference latency by over 6× compared to deeper models. The system maintains real-time performance of up to 30 FPS for a single camera and stable operation for up to four simultaneous camera streams. These results demonstrate that lightweight models provide an effective trade-off between accuracy and efficiency, making them suitable for practical edge AI deployment in fire monitoring systems.