AU-Net: an image segmentation for complex scenes

The continuous advancement of artificial intelligence technology has made autonomous driving possible. However, duo the lack of sufficient data to train a good deep learning model, the current smart driving system can only rely on the driver for autonomous control, which may have serious consequences in the event of an accident. In practical applications, smart driving systems not only need autonomous driving technology, but must also be able to recognize obstacles and accurately avoid them without relying on manual manipulation, making the integration of autonomous driving features into vehicles a very promising research direction. To address this problem, we propose a novel segmentation method, AU-Net, which is capable of achieving accurate and complete segmentation of complex scenes by introducing an axial attention mechanism. We evaluate the performance of our model on the dataset Camvid, which improves 0.54%, 0.47%, 0.32% and 1.54% in the miaou, accuracy, percision and recall metrics, respectively, and the results show that our model is well adapted to complex scenes in intelligent driving detection.