基于改进YOLOv8的低气压燃气火焰状态识别方法

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中图分类号：TP391.41 文献标志码：A

Improved YOLOv8 for gas flame state recognition under low-pressure conditions

SAl Qingyi1，ZHAO Jin1，YAN Yonghui1，BI Degui² （1.SchoolofEnergyandPowerEngineering，UniversityofShanghaiforScienceandTechnologyhanghai 2o93，China; 2.SchoolofEnvironmentandArchitecture，UniversityofShanghaiforScienceandTechnologyShanghai 2Oo93，China）

Abstract： Gas flame detection in high-altitude low-pressure environments faces dual challnges of insufficient recognition accuracy and real-time performance degradation. This study proposes an improved lightweight YOLOv8n algorithm with multi-dimensional optimizations for enhanced performance. First， a GhostConv module was constructed in the backbone to reduce computational parameters. Second， the C2f module was restructured into a C2f_RepGhost configuration that maintained feature representation capability while simplifying inference processes. Finaly， the convolutional block attention module （CBAM） attention mechanism was incorporated to strengthen fine-grained flame feature extraction， and the WIoU loss function was adopted to improve localization accuracy. Experimental results based on mechanism testbed data demonstrate that the improved model's parameter count decrease by 12.64% ， computational load decrease by 12.2% ， and precision increase by 21.2% while maintaining frame rate detection. This method provides an effective lightweight solution for flame state recognition in low-pressure environments.

Keywords： low-pressure; flame detection; deep learning; YOLOv8; attention mechanism

以甲烷为主要燃料的燃气炉对燃烧稳定性的要求极高，不稳定燃烧或熄火现象极易触发爆炸事故。（剩余11198字）