Automatic driving scene-oriented semantic segmentation method based on deep learning

The invention discloses an automatic driving scene-oriented semantic segmentation method based on deep learning, and mainly solves the problems of large calculation amount and low segmentation accuracy in a current streetscape image semantic segmentation technology. According to the method, Cityscapes and Camvid data sets are used as a training set and a test set, the data sets are preprocessed ina Tensorflow environment, then an improved Xception classification model is used as a trunk network, feature extraction is performed on a target object in a complex scene image, an Xception recognition processing result is sent to DeeplabV3 + for semantic segmentation, and finally, a segmentation result is obtained after training, testing and network parameter adjustment. In the method, the improved Xception is used as a classification network model, so that the accuracy of image target recognition and segmentation is improved, and the recognition time and economic cost are reduced. The method can be applied to the f