Abstract
From the visual scenes, detection of smoke and fire is a challenging task and many approaches have been proposed for the classification of smoke and fire. However, an intelligent image-based system for fire and smoke detection is crucial to prevent large-scale fire events in the world. Rule-based conventional algorithms are not very sufficient to perform these types of detections in real-world due to manual feature engineering and other issues like complex images, similar intensities, variations in objects shape and size, etc. To overcome these issues, DeepFireNet model is proposed. DeepFireNet model performs automatic feature engineering as it is totally based convolutional neural networks (CNN). A generalized dataset is collected and then DeepFireNet model is trained on the raw pixels of the images belonging to the fire and smoke category. The proposed method outperforms, when it is compared with the state-of-the-art methods like AlexNet, Squeeze Net, and Fire Detection Model. The proposed method is achieved an accuracy of 92.33% on an open-source dataset named ‘DeepQuestAI’. As the proposed CNN is very simple and straight forward, due to simplicity and a smaller number of layers in the network, DeepFireNet is a lightweight model and could easily deployed in hand carry devices like mobile phones, and Raspberry Pi.
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Mubeen, M., Arshed, M.A., Rehman, H.A. (2022). DeepFireNet - A Light-Weight Neural Network for Fire-Smoke Detection. In: Sanfilippo, F., Granmo, OC., Yayilgan, S.Y., Bajwa, I.S. (eds) Intelligent Technologies and Applications. INTAP 2021. Communications in Computer and Information Science, vol 1616. Springer, Cham. https://doi.org/10.1007/978-3-031-10525-8_14
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