The Road Segmentation Method Based on the Deep Auto-Encoder with Supervised Learning

Abstract

The environment perception of road is a key technique for unmanned vehicle. Determining the driving area through segmentation of road image is one of the important methods. The segmentation precisions of the existing methods are not high and some of them are not real-time. To solve these problems, we design a supervised deep Auto-Encoder model to complete the semantic segmentation of road environment image. Firstly, adding a supervised layer to a classical Auto-Encoder, and using the segmentation image of training samples as the supervised information, the model can learn the features useful for segmentation to complete the semantic segmentation. Secondly, the multi-layer stacking method of supervised Auto-Encoder is designed to build the supervised deep Auto-Encoder, because the deep network has more abundant and diversified features. Finally, we verified the method on CamVid. Compare with CNN and FCN, the road segmentation performances such as precision, speed are improved.