改进YOLOv8n的轻量化火焰烟雾检测
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1.东北石油大学三亚海洋油气研究院;2.东北石油大学人工智能能源研究院;3.东北石油大学电气信息工程学院;4.黑龙江省网络化与智能控制重点实验室

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TP391.4;TN99

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国家自然科学(62103096);海南省科技专项资助项目(ZDYF2022SHFZ105);海南省自然科学(623MS071);春晖计划项目(HZKY20220314)


Lightweight detection of flame and smoke on improved YOLOv8n
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    摘要:

    为解决火焰烟雾检测在自然环境下准确性不高,速度较慢等问题,本文提出了一种改进YOLOv8n的火焰烟雾轻量化检测算法。该算法通过将YOLOv8n中骨干网络更换为PP-LCNet轻量化网络,引入CARAFE上采样算子以增强细节捕获,减少信息丢失,并添加EMA注意力机制模块以提升模型对检测目标的识别和提取能力。实验结果表明,改进后的YOLOv8n与基准YOLOv8n相比,参数量减少0.89MB,计算量减少1.8G。此外,与Faster R-CNN、SSD、YOLOv4、YOLOv5s、YOLOv7、YOLOv8n及文献中模型相比,其精确度、召回率、mAP50和F1分别达到了96.5%、94.7%、95.3%、95.6%,表现出最佳性能。改进后的算法不仅提升了检测精确度,还实现了轻量化,具有重要的实际应用价值。

    Abstract:

    To address the issues of low accuracy and slow speed in flame and smoke detection in natural environments, this paper proposes an im-proved lightweight YOLOv8n-based detection algorithm for flames and smoke. The algorithm replaces the backbone network of YOLOv8n with the lightweight PP-LCNet network, introduces the CARAFE upsampling operator to enhance detail capture and reduce information loss, and adds the EMA attention mechanism module to improve the model’s ability to recognize and extract detection targets. Experimental results show that the improved YOLOv8n reduces the number of parameters by 0.89MB and the computational cost by 1.8G compared to the base-line YOLOv8n. Furthermore, compared with Faster R-CNN, SSD, YOLOv4, YOLOv5s, YOLOv7, YOLOv8n, and models from the liter-ature, it achieves precision, recall, mAP50, and F1 scores of 96.5%, 94.7%, 95.3%, and 95.6%, respectively, demonstrating the best perfor-mance. The improved algorithm not only enhances detection accuracy but also achieves lightweight characteristics, making it highly valuable for practical applications.

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  • 收稿日期:2024-06-12
  • 最后修改日期:2024-08-06
  • 录用日期:2024-08-07
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