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.