An Automated Learning Method of Semantic Segmentation for Train Autonomous Driving Environment Understanding

This article proposes an automated machine learning method for semantic segmentation that can be used for automated training of models in fields such as autonomous driving. This method is not specific to a particular semantic segmentation model. Users can simply upload a dataset with the semantic segmentation model they use, and then choose to use this method. This method implements end-to-end machine learning from sensing data to semantic segmentation results and model evaluation. It integrates four main components: unsupervised data reduction through feature extraction and clustering, a contrastive learning-based evaluator of semantic segmentation results, interactive reinforcement learning-based data selection, and automatic hyperparameter tuning through Bayesian optimization. We demonstrate the practicality of this method on the MRSI and Cityscapes datasets, and trained mainstream semantic segmentation models, such as BiSeNet and STDC. Our results show that this method can effectively guide semantic segmentation training and reduce the training time by more than 20 $\%$ .