Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
By Khan Muhammad, Jamil Ahmad, Zhihan Lv, Paolo Bellavista, Po Yang, Sung Wook Baik. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2019
428 citations recorded by the source corpus.
Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/37fc55957d5e0f04a9ada837cb382e67b050eb85
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In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
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Citations in the corpus
- A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing (2020, methodology): “[55] proposed a fine-tuned CNNs architecture based on the SqueezeNet model and developed a feature map selection algorithm for fire segmentation and background analysis.”