The drawback of the R-CNN and Fast R-CNN techniques is that they have two disjointed networks – one to identify the regions that likely contain an object and the other to make corrections to the bounding box where an object is identified. Furthermore, both the models require as many forward propagations as there are region proposals. Modern object detection algorithms focus heavily on training a single neural network and have the capability to detect all objects in one forward pass. In the subsequent sections, we will learn about the various components of a typical modern object detection algorithm:
- Anchor boxes
- Region proposal network (RPN)
- Region of interest pooling
So far, we have had region proposals coming from the selectivesearch method. Anchor boxes come in as a handy replacement for selective search – we will learn how they replace selectivesearch-based region proposals in this section.