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)