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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

2015 · arXiv

Computer visionMachine learningAlgorithms
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From the abstract

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

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Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels

4 selected excerpts · 4 total mentions

edshiro
Recommendation · rule
Good work! I did this project as well about a week ago. I will shamelessly also share my writings on this project: https://medium.com/towards-data-science/teaching-cars-to-see... . The HOG + SVM method is quite slow and not as accurate as a deep learning approach. Before jumping onto semantic segmentation, I recommend re-implementing this project or more generally solve this problem using a Regional Convolution Neural Network architecture (R-CNN) like Faster R-CNN[1] or YOLO[2] for instance. [1]: https://arxiv.org/abs/1506.01497 [2]: https://arxiv.org/abs/1506.02640
Original comment Show HN: Vehicle Detection – using machine learning and computer vision
joshvm
Neutral mention · rule
Absolutely you can do 'AI Research' without a degree in maths. Sure you need a grounding in linear algebra, stats, probability and calculus, but not much more than a CS or physics degree will teach you. That stuff is indeed learned easily, and it's not what the Reddit user is worried about. That also ignores applications of machine learning, which is also a massive (and lucrative) field. But because it's a trendy field, I think there is an obsession with people needing to understand everything theoretical that comes out for fear of missing the boat. Some of the really interesting papers that have come out over the last few years - for example artistic style transfer and…
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Faster R-CNN - have hardly any maths in. You can count the equations on one hand in both those papers. No doubt the authors know their stuff, but how readable are those papers compared to e.g. a 100-page proof? Which did I learn more from? They're a combination of two things: intuitive network architecture and a clever loss function. The first thing is a combination of intuition and programming, the second involves a little maths, but nothing outrageous. [1] https://arxiv.org/abs/1508.06576 [2] https://arxiv.org/abs/1506.01497
Original comment Confession as an AI researcher; seeking advice
maffydub
Neutral mention · rule
To save someone googling, the referenced "Is object detection for free" paper is at http://leon.bottou.org/publications/pdf/cvpr-2015.pdf I also just came across "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", which looks interesting and is at https://arxiv.org/pdf/1506.01497.pdf (I'll be reading these after work tonight.)
Original comment YOLO: Real-Time Object Detection
edshiro
Neutral mention · rule
There are quite a few papers that discuss the application R-CNN (Region-based Convolutional Neural Network) for object detection and classification [1-3]. On websites such as Medium, you can find lots of blog posts on object detection using popular frameworks such as Tensorflow (e.g. https://towardsdatascience.com/how-to-train-your-own-object-...). [1] Faster R-CNN: https://arxiv.org/abs/1506.01497 [2] Single Shot Multibox: https://arxiv.org/abs/1512.02325 [3] YOLO9000: https://arxiv.org/abs/1612.08242
Original comment Using CNNs for Handbag Brand and Color Detection

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