A Residual Encoder-Decoder Network for Semantic Segmentation in Autonomous Driving Scenarios

In this paper, we propose an encoder-decoder based deep convolutional network for semantic segmentation in autonomous driving scenarios. The architecture of the proposed model is based on VGG16 [1]. Residual learning is introduced to preserve the context while decreasing the size of feature maps between the stacks of convolutional layers. Also, the resolution is preserved through shortcuts from the encoder stage to the decoder stage. Experiments are conducted on popular benchmark datasets CamVid and CityScapes to demonstrate the efficacy of the proposed model. The experiments are corroborated with comparative analysis with popular encoder-decoder networks such as SegNet and Enet architectures demonstrating that the proposed approach outperforms existing methods despite having fewer trainable parameters.