Benchmarking pre‐trained Encoders for real‐time Semantic Road Scene Segmentation

Semantic segmentation, i.e. assigning each pixel in an image a class to which it belongs, can be a part of the implementation for the perception model of autonomous vehicles. Over the last years multiple powerful neural network architectures for solving this task have been developed. In this work, a simple lightweight but modular, fully convolutional encoder‐decoder network has been implemented and multiple, publicly available pre‐trained classification networks are evaluated on their encoder performance on the Cityscapes [8] benchmark dataset with respect to their accuracy and their inference speed.