Selective Search vs RPN: Which Region Proposal Algorithm is Best?

Selective Search and RPN (Region Proposal Network) are both commonly used algorithms in object detection, but they have some differences.

Selective Search is a traditional region extraction algorithm that generates a series of candidate regions by segmenting and merging the image. These candidate regions are then sent to a classifier for object detection. Selective Search was widely used in early object detection algorithms, but its efficiency is low and it's not suitable for large-scale datasets.

RPN is a deep learning-based region extraction algorithm, which is the core part of the Faster R-CNN algorithm. RPN can directly generate a series of candidate regions on the image and send them to a classifier for object detection. Compared to Selective Search, RPN is more efficient and accurate, suitable for large-scale datasets.

Overall, Selective Search is a traditional region extraction algorithm, while RPN is a deep learning-based region extraction algorithm. Both have their pros and cons, and the choice of which algorithm to use depends on the specific application scenario and requirements.