Image Segmentation Based On U-Net and Adjusted U-Nets

Abstract

Image segmentation, the process of dividing an image into various areas is quite crucial recently. Numerous industries, including robotics, remote sensing, and medical imaging, have applications for this task. In recent years, deep learning techniques, especially U-shaped networks (U-nets), have shown remarkable success in solving image segmentation problems. This paper provides an overview of image segmentation using neural networks, introduces different types of adjusted U-nets used for this task, including the implementation of attention gates and the use of residual neural network as the encoder path based on the original encoder-decoder structure, and then uses U-net and adjusted U-nets to conduct image segmentation on the black sea sprat. The study uses dice similarity coefficient and binary cross entropy loss function to compare the model training results and further judges the functionality of the models by the predicted segmented images. According to the test results, the Res34-UNet with attention gates performs most efficiently in segmenting this image dataset, although it's more unstable compared to the basic U-Net.