How Fast R-CNN works on object detection?

Introduction to Fast R-CNN

Photo by Edward Ma on Unsplash

This is the second story for R-CNN series. You may understand more about R-CN from here. Fast R-CNN (Region-based Convolutional Neural Network) is designed to tackle the object detection problems.

This story will discuss Fast R-CNN (Girshick, 2015), and the following will be covered:

• The architecture of Fast R-CNN
• Region-of-Interest Pooling (RoIPool)
• Model Training
• Experiment

Architecture

Giving an image and region proposals, it will passing thought convolutional network, Region-of-Interest (RoI) polling, fully connected network networks (FC) and the final output are the probability of object class and corresponding bounding box positions.

Fast R-CNN Network Architecture (Girshick R., 2015)

To prevent missing lots of objects, it is intended to have a high recall in finding region proposals. However, it impacts the performance in object detection parts. RoI comes to address this issue by choosing suitable region proposals.

Region-of-Interest Pooling (RoIPool)