Abstract
Fire hazards have increased in recent years and detecting fire at an early stage is of utmost importance. An upward smoke movement can help identify location of a fire incident. Therefore smoke detection using vision based machine learning techniques have been quite useful. Recent techniques deploy deep learning models for smoke detection in an outdoor environment. Despite advancements in the field, smoke detection in challenging environments is still a concern in real time applications. Further, deep learning models have large memory footprint that hinders their usage in IoT based smoke detection systems. In this paper, a convolutional neural network with attention mechanism and residual learning is proposed for smoke detection using images of outdoor scenes. The model is lightweight with only 1.23 million parameters, reasonably lower than the existing deep learning models. The model achieves a detection rate of \(99.13\%\), and an accuracy of \(99.20 \%\) on a publicly available dataset. Its performance is also compared with eight existing deep learning smoke detection models that shows its superiority over other models. Features extracted through the model are clustered using t-SNE visualization technique to demonstrate the model’s efficacy in distinguishing features of smoke and non-smoke images.
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Chaturvedi, S., Khanna, P., Ojha, A. (2023). An Efficient Residual Convolutional Neural Network with Attention Mechanism for Smoke Detection in Outdoor Environment. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_1
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