Design of Smoke-temperature Composite Detector Based on HWD32

Zhang, Huixiang; Zhang, Hong; Yin, Nan

doi:10.1109/itnec56291.2023.10082156

Cited by 2 publications

(1 citation statement)

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“…While these approaches are straightforward to implement, their accuracy is limited. To address these limitations, composite detectors are widely used nowadays [4]. Some scholars collect smoke concentration, temperature, carbon monoxide (CO) concentration, images and other information, and apply information fusion technology to fuse the heterogeneous data of multi-sensors [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural Network

Deng,

Shi,

Wang

et al. 2023

Sensors

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Indoor fires pose significant threats in terms of casualties and economic losses globally. Thus, it is vital to accurately detect indoor fires at an early stage. To improve the accuracy of indoor fire detection for the resource-constrained embedded platform, an indoor fire detection method based on multi-sensor fusion and a lightweight convolutional neural network (CNN) is proposed. Firstly, the Savitzky–Golay (SG) filter is used to clean the three types of heterogeneous sensor data, then the cleaned sensor data are transformed by means of the Gramian Angular Field (GAF) method into matrices, which are finally integrated into a three-dimensional matrix. This preprocessing stage will preserve temporal dependency and enlarge the characteristics of time-series data. Therefore, we could reduce the number of blocks, channels and layers in the network, leading to a lightweight CNN for indoor fire detection. Furthermore, we use the Fire Dynamic Simulator (FDS) to simulate data for the training stage, enhancing the robustness of the network. The fire detection performance of the proposed method is verified through an experiment. It was found that the proposed method achieved an impressive accuracy of 99.1%, while the number of CNN parameters and the amount of computation is still small, which is more suitable for the resource-constrained embedded platform of an indoor fire detection system.

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Section: Introductionmentioning

confidence: 99%