RoboCup SPL instance segmentation based on improved U-Net

Semantic segmentation is often used in robots' environment perception and object recognition, which can help robots better understand the surrounding environment and perform correct tasks. In the semantic segmentation of football robots, the semantic segmentation model based on the U-Net model works better in the context of football robot competitions, but there is still room for optimization. In response to the special situational needs of football robots, this paper carries out optimization measures such as data enhancement, adding an attention mechanism, and changing the physical mark extraction model based on the U-Net model, and the overall performance has been improved. On the official data set provided by RoboCup SPL, the accuracy rate finally reached 96.25%.