基于深度学习的火焰分割模型对比研究

由于火焰分割数据集欠缺,经典语义分割模型在火焰分割的研究应用面小,模型对比实验不充分。针对这些问题,在构建火焰分割数据集的基础上,选用在公开数据集中表现良好的4种语义分割模型和2种骨干网络进行训练和测试,并在不同的应用场景下进行对比实验及分析。实验结果表明,U-Net模型在火焰分割领域取得了较好的效果,其中U-Net+Resnet50模型的综合效果最佳,U-Net+Mobilenet V2模型综合效果略差,但运行速度更快。

Comparative research on flame segmentation models based on deep learning

Due to the lack of flame segmentation data set, the application of traditional image segmentation methods on flame segmentation study is inadequate, and the model comparison test is not enough. To deal with these problems, based on the construction of the flame segmentation data set, 4 kinds of semantic segmentation models and 2 kinds of backbone networks which perform well in public dataset were chosen for training and testing, and were compared and analyzed under different application scenario. Experimental results showed that, U-Net model has better effect in the flame segmentation, in which U-Net+Resnet50 has the best comprehensive effect, while U-Net+Mobilenet V2 has slightly worse effect, but higher running speed.