Electrode Microscopic Image Segmentation Method by Fusing Multi-layer Perceptual Attention
To address the problems of blurred material edges,artifacts,and uneven grayscale in electrode microscopic images of NOx sensors,an electrode microscopic image semantic segmentation method that fuses multi-layer perceptual attention is proposed,in which U-Net is the base model.First,different scale output feature maps of the U-Net encoding layer with a 3×3 convolution are used to reduce dimensionality.Furthermore,bilinear interpolation is used to unify feature scales to achieve multi-scale feature fusion,enhance feature information extraction,and compensate for feature loss from encoding downsampling.Second,by adding spatial pyramid pooling to extract multi-scale information and employing a 1×1 convolution to reduce the calculation,a multi-layer perceptual attention module is proposed to capture the spatial position and channel dependence of the backbone feature map and the feature map with enhanced semantic information.Finally,a loss function with the ability to capture spatial similarity is proposed based on the similarity relationship of feature maps with different semantic information combined with cross-entropy loss.The key information is supervised during the training process to assist the backbone feature map to learn spatial position information and enhance the segmentation performance.The experimental results indicate that the Mean Pixel Accuracy(MPA)of the proposed method is 96.75%,the Mean Intersection over Union(MIoU)is 94.04%,Micro-F1 is 96.92%,FLOPs is 7.78×109,and the number of parameters contained in the network is 8.08×106.Compared with models such as U-Net and SegNet,the proposed method can effectively address problems of edge blurring and material artifacts while increasing a little model complexity.Furthermore,it can capture spatial position and channel information,preserve detailed features of the image,and improve segmentation accuracy.
国家科技期刊平台
NSTL
万方数据
维普
