Semantic Segmentation Based on Dividing Different Difficulty Class Levels

The task of semantic segmentation is to classify each pixel of a given picture, in order to solve the problem that the accuracy of semantic segmentation will drop sharply when the number of classes increases sharply or the characteristics of classes change frequently. This research proposes to divide the pixel classification task into different subtasks according to the classification difficulty. The specific work is divided into two parts: training a neural network for each subtask and training an integrated neural network. Divide the number of difficulty levels according to the number of image pixels. By training the neural network for each different difficulty level, the probability map of each subtask can be obtained, and then the integrated network can be trained through these probability maps. In the experimental part, this research divides the 11 categories on the data set into three categories: easy, medium, and difficult for training. The average IoU is used on the CamVid data set to measure the accuracy of semantic segmentation of this method. The experimental results show that this research method has a 2% improvement in the average IoU of various types compared with the single U-net compared with the traditional method. Especially in the three categories of fences, sidewalks, and cyclists, there is an increase of more than 5%.