> For the complete documentation index, see [llms.txt](https://jgoodman8.gitbook.io/iron-data-science-notebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jgoodman8.gitbook.io/iron-data-science-notebook/ml-datascience/computer-vision/object-detection/two-stage-detectors/traditional-detection-models.md).

# Traditional Detection Models

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*Faster R-CNN Explained  for Object Detection Tasks (*[*DigitalOcean*](https://www.digitalocean.com/community/tutorials/faster-r-cnn-explained-object-detection)*)*
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Normally, the traditional detection pipeline follows three major tasks:

* **Region Proposal**: identify candidate regions potentially containing objects (> 2.000).
  * Selective Search
  * Edge Boxes
* **Feature Extraction**: fixed-length feature vector using image descriptors.
* **Classification**: to assign the background class or one of the object classes to detect.  Initially using an SVM classifier.