Abstract
This paper implemented image classification for smoke and flame detection. CNN model was trained in three topologies of InceptionV3, MobileNet, and VGG16. These three models were then tested on Raspberry Pi 4 with Intel Neural Compute Stick 2 (NCS 2). The experimental results demonstrated that MobileNetV2 is a superior model to the other two models in terms of training and inference, even if the accuracy rate of the three was as high as 94% when utilizing the test set for evaluation.
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Acknowledgements
This work was supported by the National Science and Technology Council (NSTC), Taiwan (R.O.C.), under grants number 111-2622-E-029-003-, 111-2811-E-029-001-, 111-2621-M-029-004-, and 110-2221-E-029-020-MY3.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Kristiani, E., Chen, YC., Yang, CT., Li, CH. (2023). Image Classification for Smoke and Flame Recognition Using CNN and Transfer Learning on Edge Device. In: Deng, DJ., Chao, HC., Chen, JC. (eds) Smart Grid and Internet of Things. SGIoT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-31275-5_16
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DOI: https://doi.org/10.1007/978-3-031-31275-5_16
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