A Lightweight Fire Detection Framework for Edge Visual Sensors Using Small-Sample Domain Adaptation

TL;DR

This paper introduces a fire detection system for edge sensors that adapts to different lighting conditions with minimal labeled data.

Contribution

A novel small-sample domain adaptation method for fire detection in vision-based sensor networks.

Findings

The proposed method increases F1-score by 19% in typical daytime scenarios.

It achieves a 30% improvement in F1-score for nighttime cross-domain scenarios.

The framework is suitable for resource-constrained edge computing nodes.

Abstract

Addressing the challenges in vision-based sensor networks, this study proposes a novel fire detection framework combining Multi-Feature Fusion and Adaptive Support Vector Machine (A-SVM). First, a high-dimensional feature vector is constructed by fusing HSI color space statistics, Local Binary Pattern (LBP) dynamic textures, and Wavelet Transform shape features. A baseline SVM classifier is then trained on source domain data. Second, to overcome the difficulty of acquiring labeled samples in target domains (e.g., strong daytime interference or low nighttime illumination), a small-sample domain adaptation mechanism is introduced. This mechanism fine-tunes the source model parameters using only a few labeled samples from the target domain via regularization constraints. Experimental results demonstrate that, compared with traditional color thresholding methods and unadapted baseline SVMs, the proposed method increases the F1-score by 19% and 30% in typical daytime and nighttime cross-domain scenarios, respectively. This study effectively achieves low-cost, high-precision, and robust cross-scenario fire detection, making it highly suitable for deployment on resource-constrained edge computing nodes within smart sensor networks.

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