In this chapter, we learned how to apply semantic segmentation using OpenCV, deep learning, and the ENet architecture. We used the pretrained ENet model on the Cityscapes dataset and performed semantic segmentation for both images and video streams. There were 20 classes in the context of SDCs and road scene segmentation, including vehicles, pedestrians, and buildings. We implemented and performed semantic segmentation on an image and a video. We saw that the performance of ENet is good for both videos and images. This will be one of the great contributions to making SDCs a reality as it helps them detect different types of objects in real time and ensures the car knows exactly where to drive.
In the next chapter, we are going to implement an interesting project called behavioral cloning. In this project, we are going to apply all the computer vision and deep learning knowledge we have gained from the previous chapters.
