Body: <p>Is there a good way to understand how single-shot object detection works? The most basic way to do detection is use a sliding-window detector and look at the output of the NN to detect if a class is there or not.  </p>
  
  <p>I'm wondering if there is a way to understand how many of the single-shot detectors work? Internally is there some form of sliding window going on? Or is it basically the same detector learned at each point?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-03T05:42:25.390Z
Comments.Text: Single shot detectors are very black box, so you're not going to know how it works internally, all you can look at is the structure. I suggest looking at YOLO, and how it operates. You can find out more here: https://towardsdatascience.com/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006
Comments.UserDisplayName: Recessive
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-03T06:13:58.230Z
Comments.Text: Most have internal regression layer which produce object box coordinates.
Comments.UserDisplayName: mirror2image
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-03T06:57:07.233Z
Comments.Text: Most start with pretrained bounding boxes which are then regressed/removed/classified etc. as the layers go deeper. Most SSD's have different parts such as feature proposal and bounding box regressor, whereas yolo is a single network, SSD has multiple parts which you might have to look into.
Comments.UserDisplayName: SajanGohil
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-07T05:45:23.237Z
Comments.Text: As an obvious question, if the item of interest is moved some place else on the picture is the detection performance the same? It seems like a successful SSD would be very hard to train since position information is encoded in image which would result in poor generalization.
Comments.UserDisplayName: FourierFlux
Tags: <deep-learning><computer-vision><yolo>
Title: Intuition behind single-shot object detection
UserDisplayName: FourierFlux
ThreadText: # Intuition behind single-shot object detection
  
  (Asked by: username_0 on 2020-03-03)
  
  Is there a good way to understand how single-shot object detection works? The most basic way to do detection is use a sliding-window detector and look at the output of the NN to detect if a class is there or not.
  
  I'm wondering if there is a way to understand how many of the single-shot detectors work? Internally is there some form of sliding window going on? Or is it basically the same detector learned at each point?
  
  **Tags:** deep-learning, computer-vision, yolo
  > **Comments:**
  > **username_1**: Single shot detectors are very black box, so you're not going to know how it works internally, all you can look at is the structure. I suggest looking at YOLO, and how it operates. You can find out more here: https://towardsdatascience.com/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006  
  > **username_2**: Most have internal regression layer which produce object box coordinates.  
  > **username_3**: Most start with pretrained bounding boxes which are then regressed/removed/classified etc. as the layers go deeper. Most SSD's have different parts such as feature proposal and bounding box regressor, whereas yolo is a single network, SSD has multiple parts which you might have to look into.  
  > **username_0**: As an obvious question, if the item of interest is moved some place else on the picture is the detection performance the same? It seems like a successful SSD would be very hard to train since position information is encoded in image which would result in poor generalization.  
  
  ---
  
  ## 0 answers
  
  