I would like to know where I could find useful articles/papers about the basic concepts of RPN and R-CNN.


1 Answer 1


There are many articles and papers that cover the basic concepts of RPN and R-CNN. Here are a few sources that you may find useful:

  • The original RPN and R-CNN papers by Girshick et al. provide a detailed description of the algorithms and their performance on various object detection tasks:

    • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580-587).
    • Girshick, R., Iandola, F., Darrell, T., & Malik, J. (2015). Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1440-1448).
  • The official TensorFlow Object Detection API tutorial provides a high-level overview of RPN and R-CNN, along with code examples and implementation details.

  • The blog post Object detection with R-CNN by Kaiming He provides a brief but informative introduction to R-CNN and its applications in object detection:

  • The paper R-CNN, Fast R-CNN, and Faster R-CNN Explained by Yuxin Wu provides a comprehensive review of the RPN and R-CNN algorithms, including their evolution and performance on various benchmarks.

  • A blog post by Adrian Rosebrock that provides a high-level overview of RPN and R-CNN.

In general, RPN and R-CNN are used in object detection systems to generate high-quality region proposals and then classify those proposals using a convolutional neural network. RPN uses a sliding window approach to generate region proposals, while R-CNN uses a region proposal network to generate proposals and then applies a convolutional neural network to classify them. Both approaches have been shown to be effective for object detection tasks.

These resources should provide a good starting point for understanding the basic concepts of RPN and R-CNN.


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