Object Detection – Part 3: Fast R-CNN

In the previous post, we had an in-depth overview of Region-based Convolutional Neural Networks (R-CNN), which is one of the fundamental architectures in the Two-Stage Object Detection pipeline approach. During the ramp-up of the Deep Learning era in around 2012 when AlexNet was published, the approach of solving the Object Detection problem changed from hand-built features like Haar features and Histogram of Oriented Gradients approaches to the Neural Network-based approach, and in that mainly the CNN-based architecture. Over time it has been solved via a 2-Stage approach, where the first stage will be mainly based on generating Region Proposals, and the second stage deals with classifying each proposed region.