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

Deng, Xinwei; Shi, Xuewei; Wang, Haosen; Wang, Qianli; Bao, Jun; Chen, Zhuming

doi:10.3390/s23249689

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…합하여 검출하는 방식이 꾸준히 개발되어 왔다 등 . Baek (10) (11) 은 다양한 화재 검출 감지기를 무선 네트워크로 구성하고 퍼지 논리 규칙으로 처리하 (fuzzy) 는 시스템을 제안하였다 (12) (13,14) .…”

Section: 관련 연구unclassified

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Kang,

Kwon,

Cho

et al. 2024

Fire Sci. Eng.

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Pre-prediction and early detection are important for the prevention of fires. Hence, the integration of intelligent CCTV image analysis technology, deep learning, and artificial intelligence is actively being studied. Intelligent CCTV can analyze visible light images of fires, but the accuracies of the detection targets are low. To solve this problem, a dual sensing experimental device was constructed by combining a visible light sensor and a thermal image sensor, and an algorithm was implemented to predict fire events. An algorithm was constructed to detect three classes (Fire, Smoke, and Person) through RGB sensors and thermal imaging sensors. Additionally, a CNN deep learning method and the YOLOv5 method were used to build a classification neural network. The dual sensing device was able to act as a trigger to predict and detect a fire based on the size of the flame and changes in temperature.

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Section: 관련 연구unclassified

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Kang,

Kwon,

Cho

et al. 2024

Fire Sci. Eng.

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“…After multiple training iterations, the model's performance is gradually improved. Before modeling, the spectral data were preprocessed using the Savitzky-Golay (SG) [34][35][36] and standard normalized variate (SNV) [37][38][39] algorithm to remove noise and background effects from the spectral data. Through leave-one-out cross-validation and sk cross-validation with 2-fold, 5-fold, and 10-fold configurations, we identified the optimal cross-validation approach for the current dataset.…”

Section: Convolutional Autoencodermentioning

confidence: 99%

QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin

Ma,

Li,

Peng

et al. 2023

Forests

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Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources of Salix psammophila exhibit significant variations in their effectiveness for windbreak and sand stabilization. Therefore, it is essential to establish a rapid and reliable method for identifying different Salix psammophila varieties. Visible and near-infrared (Vis-NIR) spectroscopy is currently a reliable non-destructive solution for origin traceability. This study introduced a novel feature selection strategy, called qualitative percentile weighted sampling (QPWS), based on the principle of the long tail effect for Vis-NIR spectroscopy. The core idea of QPWS combines weighted sampling and percentage wavelength selection to identify key wavelengths. By employing a multi-threaded parallel execution of multiple QPWS instances, we aimed to search for the optimal feature bands to address the instability issues that can arise during the feature selection process. To address the problem of reduced prediction performance in one-dimensional convolutional neural network (1D-CNN) models after feature selection, we have introduced convolutional autoencoders (CAEs) to reduce the dimensions of wavelengths that are discarded during feature selection. Subsequently, these reduced dimensions are fused with the selected wavelengths, thereby enhancing the model’s performance. With our completed model, we selected outstanding models for model fusion and established a decision system for Salix psammophila. It is worth noting that all 1D-CNN models in this study were developed using Bayesian optimization methods. In comparison with principal component analysis (PCA) and full spectrum methods, QPWS exhibits superior predictive performance in the field of machine learning. In the realm of deep learning, the fusion of data combining QPWS with CAE demonstrated even greater potential with an improvement of average accuracy of approximately 2.13% when compared to QPWS alone and a 228% increase in operational speed compared to a model with full spectra. These results indicated that the combination of CAE with QPWS can be an effective tool for identifying the origin of Salix psammophila.

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Real-Time prediction of pool fire burning rates under complex heat transfer effects influenced by ullage height: A comparative study of BPNN and SVR

Gao,

Ji,

Wang

et al. 2024

Thermal Science and Engineering Progress

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

Cited by 5 publications

References 23 publications

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin

Real-Time prediction of pool fire burning rates under complex heat transfer effects influenced by ullage height: A comparative study of BPNN and SVR

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