Improved Image Segmentation Algorithms based on Convolutional Neural Networks

Image segmentation techniques based on Full Convolutional Neural Networks (FCNs) have the capability to directly process images of arbitrary sizes. This eliminates the need for complex intermediate steps and achieves end-to-end pixel-level segmentation, thereby enhancing segmentation accuracy. This paper, after an in-depth study of image segmentation techniques and convolutional neural networks, integrates the FCN technique with image segmentation technology. It constructs an FCN network based on the ResNet network, accomplishing pixel-level segmentation of images. To overcome the limitations of the original FCN network, specifically its mediocre performance on deep neural networks and excessive downsampling rates, certain improvements have been implemented. Dilated convolutions are employed to expand the receptive field during convolution, facilitating the learning of more intricate features.