The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
Open accessAbstract
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.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201
Region-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912
PCANet: A Simple Deep Learning Baseline for Image Classification?
Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2475625 · Cited by 1,362
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817
An early fire-detection method based on image processing
Chen, T.-H., Wu, P.-H., & Chiou, Y.-C. (2005) · 2004 International Conference on Image Processing, 2004. ICIP '04. · doi:10.1109/icip.2004.1421401 · Cited by 644
Computer vision based method for real-time fire and flame detection
Töreyin, B. U., Dedeoğlu, Y., Güdükbay, U., & Çetin, A. E. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.06.015 · Cited by 639
Muhammad, K., Ahmad, J., & Baik, S. W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.04.083 · Cited by 558 · cited 1×
“, can 34 be detected at early stages, and the appropriate authority 35 can be autonomously informed [6], [7].”
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Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. (2019) · Journal of Computational Science · doi:10.1016/j.jocs.2018.12.003 · Cited by 891
Self-citationHashemzadeh, M., & Zademehdi, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.019 · Cited by 100
Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN
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Metadata
- Authors
- Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., & Baik, S. W.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2019-07
- Volume
- 49
- Issue
- 7
- Pages
- 1419-1434
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2807862495
- Cited by
- 537
- References
- 45
- FWCI
- 50.41
- Type
- journal article
- Language
- en
- Open access
- green
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- Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., & Baik, S. W. (2019). Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2018.2830099
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