Object Detection using R-CNN, Fast R-CNN, and Faster R-CNN | Computer Vision Hands-on Bootcamp скачать в хорошем качестве

Object Detection using R-CNN, Fast R-CNN, and Faster R-CNN | Computer Vision Hands-on Bootcamp

If you wish to be part of our PRO cohort, join here: https://hands-on-cv.vizuara.ai/ In our recent lecture, we traced the evolution of three landmark models in object detection — RCNN, Fast RCNN, and Faster RCNN. Understanding these models side-by-side is essential to appreciate how object detection progressed from an accurate but slow pipeline to a fast and practical deep learning approach. RCNN (2014) The original RCNN or Region-based Convolutional Neural Network was a breakthrough because it was the first to combine traditional region proposal methods with CNN-based feature extraction. The pipeline had three main steps: Generate ~2000 region proposals using Selective Search (a classical algorithm, not learned). Run a CNN separately on each proposed region to extract features. Classify these features with an SVM and refine the bounding box coordinates with a regression layer. RCNN achieved a big leap in accuracy compared to traditional methods, but it was painfully slow — every proposal required a forward pass through the CNN, which made real-time detection impossible. The training process was also multi-stage and computationally heavy. Fast RCNN (2015) Fast RCNN addressed the main inefficiency of RCNN — the repeated CNN computation for each region. Instead of cropping and resizing each proposal at the image level, Fast RCNN ran the CNN once on the full image to produce a convolutional feature map. Then, it applied RoI (Region of Interest) pooling to extract fixed-size feature maps for each region proposal directly from this shared feature map. These pooled features went through fully connected layers to produce classification scores and bounding box regression in a single network. This architectural change eliminated the need to run the CNN thousands of times per image and unified the classification and regression into a single training process. The region proposals, however, still came from Selective Search, which remained slow. Faster RCNN (2015) Faster RCNN took the next logical step — removing the dependency on the slow Selective Search algorithm altogether. It introduced the Region Proposal Network (RPN), a small network that learns to generate region proposals directly from the convolutional feature maps. This meant that region proposal generation became part of the deep learning pipeline itself, and was extremely fast. The RPN shares convolutional features with the detection network, which means the whole process from proposal to classification and bounding box regression happens in a single, end-to-end trainable framework. This design allowed Faster RCNN to achieve near real-time detection speeds without compromising accuracy, and it became the backbone for many subsequent detection architectures. Key Similarities All three follow the same broad logic: generate region proposals → extract features → classify and refine boxes. All three use CNNs for feature extraction and have separate bounding box regression components. Key Differences Region proposals: RCNN and Fast RCNN use Selective Search (hand-engineered), Faster RCNN uses a learnable RPN. Feature extraction: RCNN runs the CNN for each proposal, Fast and Faster RCNN run the CNN once per image and share features. Training: RCNN has multi-stage training (CNN → SVM → regression), Fast and Faster RCNN use a single-stage, unified training process. Speed: RCNN is the slowest, Fast RCNN is significantly faster, Faster RCNN is the fastest while maintaining high accuracy. When you see this progression, it becomes clear that each step was about removing a bottleneck — first the redundant CNN passes, then the slow hand-crafted proposals. This is a perfect example of how research evolves by systematically identifying inefficiencies and replacing them with learnable, integrated solutions.