# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
###### Abstract  
Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve good performance in this field with large amounts of human-labeled training data. However, the process of collecting sufficient fine human-labeled data is extremely time-consuming and expensive. To fill this gap, in this paper, we innovatively propose a general yet effective semi-supervised method for drivable road segmentation with lower labeled-data dependency, high accuracy, and high real-time performance. Specifically, a main encoder and a main decoder are trained in the supervised mode with labeled data generating pseudo labels for the unsupervised training. Then, we innovatively set up both auxiliary encoders and auxiliary decoders in our model that yield feature representations and predictions based on the unlabeled data subjected to different elaborated perturbations. Both auxiliary encoders and decoders can leverage information in unlabeled data by enforcing consistency between predictions of the main modules and those perturbed versions from auxiliary modules. Experimental results on two public datasets (Cityspace and CamVid) verify that our proposed algorithm can almost reach the same performance with high FPS as a fully supervised method with 100% labeled data with only utilizing 40% labeled data in the field of drivable road segmentation. In addition, our semi-supervised algorithm has a good potential to be generalized to all models with an encoder-decoder structure. drivable area segmentation; semi-supervised; semantic segmentation; deep learning; convolutional neural networks; feature cross-consistency +
Footnote †: journal: Medical  
hand-crafted feature extractions, which lead to vulnerable robustness in complex scenes. With the development of Convolutional Neural Network (CNN)-based models becoming mainstream in segmentation, there is no need for hand-crafted feature extractions and more advanced solutions have been designed for drivable area segmentation. In order to make the model performance as accurate as possible, it requires a massive collection of fine annotated data for training, which is time-consuming and expensive. Methods such as deep transfer learning and self-supervision can effectively reduce the dependence on labeled data [5; 6; 7], but they can not fully balance real-time results and accuracy in drivable area segmentation tasks. However, feature-perturbation-based semi-supervised methods have been proven to be effective in segmentation tasks [8]. Therefore, we propose a semi-supervised method to leverage unlabeled data for drivable area segmentation by expanding the scope of features and enforcing the consistency between the perturbed expanding features and pseudo labels, which better overcomes the insufficiency of labeled data. The model we designed consists of a main-encoder, a main-decoder, auxiliary encoders (aux-encoders), and auxiliary decoders (aux-decoders). In the following, the main-encoder and main-decoder together will be referred to as the main modules. Aux-encoders and aux-decoders together will be referred to as the auxiliary modules. Different kinds of perturbation are combined with the auxiliary modules and used in semi-supervised training. As for training, fully-supervised training is performed first, where the main modules are trained on labeled data. Semi-supervised training follows closely, as shown in Figure 1. Predictions on original unlabeled data are first made by the main modules, generating pseudo labels corresponding to the predictions made by auxiliary modules on perturbed data. Then, unsupervised loss is designed to ensure the expanded feature cross-consistency between the perturbed and pseudo labels so that the model can leverage information in unlabeled data and improve the accuracy of the main modules. We conduct experiments on the CamVid and Cityscapes datasets.