Applications of Object Detection and Segmentation

In previous chapters, we learned about various object detection techniques, such as the R-CNN family of algorithms, YOLO, SSD, and the U-Net and Mask R-CNN image segmentation algorithms. In this chapter, we will take our learning a step further – we will work on more realistic scenarios and learn about frameworks/architectures that are more optimized to solve detection and segmentation problems.

We will start by leveraging the Detectron2 framework to train and detect custom objects present in an image. We will also predict the pose of humans present in an image using a pre-trained model. Furthermore, we will learn how to count the number of people in a crowd in an image and then learn about leveraging segmentation techniques to perform image colorization. Next, we will learn about a modified version of YOLO to predict 3D bounding boxes around objects by using point clouds obtained from a LIDAR sensor. Finally, we will learn...