Sai Qingyi, Zhao Jin, Bi Degui, Qin Bo, Meng Lingshu
College of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
College of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel). 2024 Oct 2;24(19):6383. doi: 10.3390/s24196383.
This paper introduces a lightweight flame detection algorithm, enhancing the accuracy and speed of gas-flame state recognition in low-pressure environments using an improved YOLOv8n model. This method effectively resolves the aforementioned problems. Firstly, GhostNet is integrated into the backbone to form the GhostConv module, reducing the model's computational parameters. Secondly, the C2f module is improved by integrating RepGhost, forming the C2f_RepGhost module, which performs deep convolution, extends feature dimensions, and simplifies the inference structure. Additionally, the CBAM attention mechanism is added to enhance the model's ability to capture fine-grained features of flames in both channel and spatial dimensions. The replacement of CIoU with WIoU improves the sensitivity and accuracy of the model's regression loss. Experimental results on a simulated dataset of the theoretical testbed indicate that compared to the original model, the proposed improvements achieve good performance in low-pressure flame state detection. The model's parameter count is reduced by 12.64%, the total floating-point operations are reduced by 12.2%, and the detection accuracy is improved by 21.2%. Although the detection frame rate slightly decreases, it still meets real-time detection requirements. The experimental results demonstrate that the feasibility and effectiveness of the proposed algorithm have been significantly improved.
本文介绍了一种轻量级火焰检测算法，使用改进的YOLOv8n模型提高低压环境下气体火焰状态识别的准确性和速度。该方法有效解决了上述问题。首先，将GhostNet集成到主干中形成GhostConv模块，减少模型的计算参数。其次，通过集成RepGhost对C2f模块进行改进，形成C2f_RepGhost模块，该模块进行深度卷积、扩展特征维度并简化推理结构。此外，添加CBAM注意力机制以增强模型在通道和空间维度上捕捉火焰细粒度特征的能力。用WIoU替换CIoU提高了模型回归损失的灵敏度和准确性。在理论测试平台的模拟数据集上的实验结果表明，与原始模型相比，所提出的改进在低压火焰状态检测中取得了良好的性能。模型的参数数量减少了12.64%，总浮点运算减少了12.2%，检测准确率提高了21.2%。虽然检测帧率略有下降，但仍满足实时检测要求。实验结果表明，所提算法的可行性和有效性得到了显著提高。
