控制理论与应用2024，Vol.41Issue(3)：543-554,12.DOI:10.7641/CTA.2023.20987
频域多方向C-UNet及动态损失的工业烟尘图像分割
Industrial smoke image segmentation based on frequency domain multi-directional C-UNet and dynamic loss
摘要
Abstract
The accurate segmentation of the smoke in industrial smoke pollution level monitoring is an important pre-requisite for pollution level determination.The typical challenges in feature extraction of smoke include blurred edges,difficult extraction of edge directional detail information and inaccurate segmentation.In this study,a frequency domain multi-directional C-UNet(Contourlet U-Net)and dynamic loss industrial smoke image segmentation method is proposed,aiming to provide support to overcome these problems.Firstly,the contourlet multi-directional decomposition down-sampling structure is constructed to enhance the ability to extract edge direction information of smoke in the encoding stage.Secondly,the contourlet transform is used to extract detailed information on the eight edge directions of smoke for skip connections,improving the accuracy of detail information expression during continuous sampling.Then,the con-tourlet detail reconstruction up-sampling structure is constructed to enhance the recovery ability of edge detail information of smoke in the decoding stage.Finally,a dynamic weighting strategy is proposed to construct a combined loss function to optimize the training network and enhance the network's ability to extract smoke edge features.The results show that compared with U-Net and other similar methods,the proposed method has a better improvement in indicators,improves the accuracy of smoke edge segmentation,and the segmentation effect on different smoke scenes is better than the existing segmentation model.关键词
工业烟尘/图像分割/轮廓波变换/特征提取/动态损失函数Key words
industrial smoke/image segmentation/contourlet transform/feature extraction/dynamic loss function引用本文复制引用
张大锦,刘辉,陈甫刚,赵安..频域多方向C-UNet及动态损失的工业烟尘图像分割[J].控制理论与应用,2024,41(3):543-554,12.基金项目
国家自然科学基金项目(62263016,61863018),云南省科技厅应用基础研究项目(202001AT070038)资助.Supported by the National Natural Science Foundation of China(62263016,61863018)and the Applied Basic Research Programs of Yunnan Science and Technology Department(202001AT070038). （62263016,61863018）
