CenterNet


本資料は2020年8月28日に社内共有資料として展開していたものを

WEBページ向けにリニューアルした内容になります。



■Purpose

 

Purpose of this material

  • Understand an anchor free approach object detection algorithm


■Agenda

 
  • Current object detection approaches

  • Centernet approach

  • Object as Points

  • Training

  • Keypoint heatmap

  • Local offset

  • Size prediction

  • Loss function

  • Network Architecture

  • DLA

  • Modified DLA

  • Inference

  • Results



■Background

 

Current approaches


  • Object detections model (such as Yolo, SSD, etc.) rely on the usage of anchor boxes

  • Anchor boxes are not completely optimal:

  • Wasteful: SSD300 does 8732 detections per class, and yolo448 does 98 detections per class, which means that most of the box are discarded

  • Inefficient: We have to process all the boxes (even we will discard them later), which comes with more processing time

  • Require post processing: like non-max suppression algorithm

  • Fixed: SSD requires fixed scale and steps of boxes, while yolov3 fixes the size of the anchors per detection level



■Centernet

 

Centernet approach

  • End-to-end differentiable solution

  • Relies on keypoint estimation to find the center points and regress all other object properties(such as size)