Abstract: By incorporating SENet channel attention mechanism on the basis of Mobile Unet network,the image foreground subject se-gmentation algorithm is improved.The algorithm introduces deep separable convolution to reduce the number of model parameters,while utilizing skip connections and multi-scale feature fusion to improve the segmentation accuracy of the model.Du-ring the training process,a spatial pyramid pooling module with hollow convolution is used to increase the receptive field and improve the model's recognition ability for large-scale objects.Experimental results show that the improved algorithm achieves 96% MIOU(Modular Input/Output Un-it) segmentation accuracy on the PASCAL VOC2012 dataset,with an accuracy rate of 0.971,which is superior to various existing image segmentation algorithms,such as the FCN fully convolutional neural network algorithm.In terms of speed,the processing time of the model for each image is between 1.7 s and 2.5 s.The improved algorithm has a faster inference speed compared to traditional fully convolutional neural networks,making it suitable for real-time image segmentation on mobile devices.Through comparative experiments,the effectiveness of the Mobile Unet models before and after the improvement,as well as the FCN model,in foreground subject segmentation of images under bright and dim conditions is compared,and the conclusion is drawn that the improved Mobile Unet model has the best performance.Finally,the algorithm is deployed,a GUI visualization operation interface is designed,and an.exe executable file is generated.

Key words: Subject segmentation, Neural network, Receptive field, Parameter quantity, Segmentation accuracy