Segmentation applying TAG type label data and Transformer

Autonomous driving of vehicles or robots using artificial intelligence is being studied the most. The recognition of the surrounding environment is the basis for artificial intelligence that requires interaction with the surroundings, which means that research on object detection is necessary. The size of the model is smaller, and more information can be obtained than detection using anchors, but the accuracy of segmentation is generally lower. In this paper, to improve this point, a transformed transformer structure is applied to improve the performance of segmentation, and it is proposed to use data in a format different from the existing label data. By using a single image as an input, there is no loss of location information, and a lighter model is presented by obtaining a segmentation image without going through a separate process. At the same time, to improve generalization performance, a method of assigning one label to one characteristic rather than assigning one label to one object was applied to the composition of the label data, and the difference in generalization ability was compared.