On the Performance of YOLO and ML/DL Models for Lightweight, Real-Time Smoke and Fire Detection on an x86-Based Edge Platform: An Explainable Decision-Level Sensor Fusion Framework

Abstract

In this study, a decision-level detection framework is presented and evaluated; it integrates sensor data (e.g., temperature, humidity, gas readings) with machine learning (ML) models and computer vision-based smoke and fire detection systems, in an effort to increase overall robustness, as well as false-alarm reduction. To this end, sixteen (16) ML and deep learning (DL) models are employed on an Internet of Things (IoT) sensor dataset. Moreover, a range of YOLO (You Only Look Once) models, such as older versions (YOLOv5n, YOLOv8n), as well as newer versions (YOLOv10n, YOLOv11n, YOLOv12n), are employed on an image-label-based dataset. Model selection initially prioritizes lightweight architectures that are suitable for resource-constrained edge devices. Afterwards, the selected models are evaluated via well-known metrics, such as parameter count, F1-score/mean average precision (mAP) and real-time inference latency. In the same context, explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) for ML models and LIME (Local Interpretable Model-agnostic Explanations) for the YOLO detectors, are integrated into the platform as well. According to the presented results, the Explainable Sensor Fusion (ESF) framework demonstrates a high level of internal consistency and logical reliability through its decision-level fusion paradigm in a controlled environment.