A Lightweight Indoor Smoke Detector with Benchmark Dataset using Deep Learning

Abstract

A fire disaster is one of the most dangerous events that may occur at any place and time. To avoid such incidents, smoke detection is crucial before getting fire. The smoke sensors are the most widely used devices to detect fires; however, smoke sensors can only detect fires if the fire is large, and smoke reaches the sensor. Therefore, vision based smoke detectors have been proposed using machine learning and deep learning based methods. Most of these methods have been proposed for outdoor smoke detection, while little attention has been paid to indoor smoke detection due to the lack of appropriate datasets for indoor scenarios. This study creates a benchmark dataset for indoor smoke detection by properly following annotation criteria outlined by Inter Annotator Agreement (IAA) and Cohen’s Kappa evaluation metrics. The proposed dataset achieved 0.91 IAA and 0.81 Cohen’s Kappa scores, which confirms the excellent quality of the dataset. In addition to this, an innovative transfer learning-based method has been proposed for indoor smoke detection and evaluated on the proposed dataset. Furthermore, a state-of-the-art smoke detection algorithm has been implemented and evaluated on the proposed dataset for comparative analysis. The results confirm that the proposed method outperforms the state-of-the-art methods.