What's the difference between the bounding box(BB) produced by "BB regression algorithms in region-based object detectors" vs "bounding box in single shot detectors"? and can they be used interchangeably if not why?
While understanding variants of R-CNN and Yolo algorithms for object detection, I came across two major techniques to perform object detection i.e Region-based(R-CNN) and niche-sliding window based(YOLO).
Both use different variants(complicated to simple) in both regimes but in the end, they are just localizing objects in the image using Bounding boxes!. I am just trying to focus on the localization(assuming classification is happening!) below since that is more relevant to the question asked & explained my understanding in brief:
outputs precise bounding boxes.!
works in supervised learning settings, they were using labeled dataset where the labels are bounding boxes stored(manually marked my some annotator using tools like labelimg ) for each image in a JSON/XML file format.
I am trying to understand the two localization techniques on a more abstract level(as well as having an in-depth idea of both techniques!) to get more clarity on:
in what sense they are different?, &
why 2 were created, I mean what are the failure/success points of 1 on the another?.
and can they be used interchangeably, if not then why?
please feel free to correct me if I am wrong somewhere, feedback is highly appreciated! Citing to any particular section of a research paper would be more rewarding!