IEEE Access· 2018Q1
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
- 548citations
- Q1SCImago
- 2018year
Short summary
A new, cost-effective Convolutional Neural Network (CNN) architecture, inspired by GoogleNet, achieves effective fire detection in surveillance videos with lower computational demands than AlexNet.
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Key points
- Proposes a cost-effective CNN architecture for fire detection in surveillance videos.
- The model is inspired by GoogleNet, offering a balance between efficiency and accuracy.
- Fine-tuned for fire data to optimize performance for the specific task.
- Experimental results show effectiveness and suitability for CCTV systems compared to other methods.
AI-generated from the title and abstract; the full text is not read.
Abstract
The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research paper, we propose a cost-effective fire detection CNN architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as AlexNet. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.
The authors' abstract, as published at the source. IEEE Access, 2018 · DOI ↗
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Field: Safety, Risk, Reliability and Quality
Safety, Risk, Reliability and QualityEngineering