3DVSD: An end-to-end 3D convolutional object detection network for video smoke detection

Huo, Yinuo; Zhang, Qixing; Zhang, Yongming; Zhu, Jiping; Wang, Jinjun

doi:10.1016/j.firesaf.2022.103690

Cited by 14 publications

(2 citation statements)

References 11 publications

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“…Since the release of smoke is the most obvious characteristic of very early fire [ 1 ], fire smoke monitoring is considered to be the most effective means of fire warning. Therefore, the most common commercial fire detectors available are mostly based on smoke detection, such as image-based [ 2 , 3 , 4 , 5 ] and photoelectric smoke detectors [ 6 , 7 ]. Image-based smoke detection technologies determine the presence of smoke and the occurrence of fire in a target area by analyzing and processing video image information captured with a camera [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Embedded Spatial–Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification

Xu,

Zhu,

Lin

et al. 2024

Sensors

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Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial–temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial–temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.

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Section: Introductionmentioning

confidence: 99%

Embedded Spatial–Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification

Xu,

Zhu,

Lin

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…This technique is helpful for the archaeology of marine life and the conservation of marine species. In public safety, Huo et al [2] proposed a 3D convolution-based smoke detection neural network for identifying smoke features in pictures, and compared with other studies, the network achieved an accuracy of 99.54%, a false alarm rate of 1.11%, and a missed detection rate of 0.14%, and the study showed that it can meet the requirements of real-time detection and can effectively prevent fires in public places from the occurrence. In terms of safe driving, Gao et al [3] proposed an autonomous driving target detection, and experiments showed that the detection network achieved a mapping value of 60.9%, making the images clearer and the autonomous driving safer.…”

Section: Introductionmentioning

confidence: 99%