Since the proposal of Fully Convolutional Network (FCN), applying deep learning to image semantic segmentation has attracted extensive attention from researchers in the field of computer vision and machine learning, becoming a research hotspot of artificial intelligence. The core idea of FCN is to build a fully convolutional network that accepts the input of arbitrary sizes and produces the output of the same sizes through efficient inference and learning. FCN provides a new idea for image semantic segmentation, but it also has many shortcomings, such as low feature resolution and the objects at multiple scales. As research progresses, the convolutional neural network has been gradually optimized and expanded in the field of image segmentation. In addition, the mainstream segmentation frameworks based on FCN have emerged one after another. Image semantic segmentation plays an increasingly important role in scene understanding, which is widely applied to the self-driving technique, the UAV field, detection and analysis of medical images, and other tasks. Therefore, image semantic segmentation is worth further study to better serve practical applications.