Real-time pixel-wise semantic segmentation is one of the great applications of semantic segmentation for SDCs. Accuracy can increase in SDCs, but deploying semantic segmentation is still a challenge. In this section, we'll look at an efficient neural network (E-Net) that aims to run on low-power mobile devices while improving accuracy.
E-Net is a popular network due to its ability to perform real-time pixel-wise semantic segmentation. E-Net is up to 18x faster, requires 75x fewer FLOPs, and has 79x fewer parameters than existing models such as U-Net and SegNet, leading to much better accuracy. E-Net networks are tested on the popular CamVid, Cityscapes, and SUN datasets.
The architecture of E-Net is as follows:
You can check out the preceding screenshot at https://arxiv.org/pdf/1606.02147.pdf.
This is a framework with one master and several branches that split from the master but also merge back via element-wise addition. ...
