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Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux J.-M., Moreau, E. and Fnaiech, F. (2016) Convolutional Neural Network for Video Fire and Smoke Detection, IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 23-26 October 2016, 877-882.
https://doi.org/10.1109/IECON.2016.7793196

has been cited by the following article:

• TITLE: YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

  AUTHORS: Zhangchi Liu, Risheng Zhang, Hao Zhong, Yingjie Sun

  KEYWORDS: Object Recognition, CBAM, WioU, State-of-the-Art Methods

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.14 No.1, January 31, 2024

  ABSTRACT: The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.