MultiNet
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation via a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-toend and performs extremely well in the challenging KITTI dataset, outperforming the state-of-the-art in the road segmentation task. Our approach is also very efficient, allowing us to perform inference at more then 23 frames per second.
Implementations
MultiNet is able to jointly perform road segmentation, car detection and street classification. The model achieves real-time speed and state-of-the-art performance in segmentation. Check out our paper for a detailed model description.
MultiNet is optimized to perform well at a real-time speed. It has two components: KittiSeg, which sets a new state-of-the art in road segmentation; and KittiBox, which improves over the baseline Faster-RCNN in both inference speed and detection performance.
Hypes: https://github.com/MarvinTeichmann/MultiNet/tree/master/hypes
