An improved flame smoke detection algorithm for YOLOv8n

Yundong Jian; Bing Bai; Xiaozheng Li

doi:10.1117/12.3045531

20 September 2024 An improved flame smoke detection algorithm for YOLOv8n

Yundong Jian, Bing Bai, Xiaozheng Li

Proceedings Volume 13269, Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024); 1326916 (2024) https://doi.org/10.1117/12.3045531
Event: Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Given the problems of a large number of flame and smoke detection parameters and poor detection effect in the current fire warning system, this paper proposes a lightweight YOLOv8n improved algorithm. First, in terms of data sets, the Mosaic data enhancement method is used to increase the diversity of data. Secondly, the C2f module in the Backbone part is fused with the LSK attention mechanism to form a C2f-LSK module, which can increase the ability to extract flame and smoke features in complex fire scenes, thereby improving the detection accuracy. Then, for the Neck part, the weighted Bidirectional Feature Pyramid (BiFPN) is used to replace the original Path Aggregation Network (PANet) of YOLOv8, which promotes the fusion of feature maps of different scales, thereby improving the detection accuracy of the model. Experiments show that the improved YOLOv8n has high accuracy in flame and smoke detection, with precision and mAP@0.5 reaching 80.8% and 75.3% respectively, which are 3.8% and 3% higher than the original model. The improved YOLOv8n network model has higher accuracy and lower false alarm rates in flame and smoke detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yundong Jian, Bing Bai, and Xiaozheng Li "An improved flame smoke detection algorithm for YOLOv8n", Proc. SPIE 13269, Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), 1326916 (20 September 2024); https://doi.org/10.1117/12.3045531

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KEYWORDS

Flame

Object detection

Data modeling

Detection and tracking algorithms

Feature fusion

Convolution

Education and training

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