Environment perception for automated vehicles is achieved by fusing the outputs of different sensors such as cameras, LIDARs and RADARs. Images provide a semantic understanding of the environment at object level using instance segmentation, but also at background level using semantic segmentation. We propose a fully convolutional residual network based on Mask R-CNN to achieve both semantic and instance level recognition. We aim at developing an efficient network that could run in real-time for automated driving applications without compromising accuracy. Moreover, we compare and experiment with two different backbone architectures, a classification type of network and a faster segmentation type of network based on dilated convolutions. Experiments demonstrate top results on the publicly available Cityscapes dataset.
Efficient Instance and Semantic Segmentation for Automated Driving
2019 IEEE Intelligent Vehicles Symposium (IV) ; 2575-2581
2019-06-01
1942784 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch