CNN 009

Prediciting Bounding Boxes

• Using:
  • Sliding Window (Slow)
  • Selective Search
  • Region Proposals
• Task:
  • Predict Bouding boxes from CNN

Non Maxima Suppression (NMS)

1. Check the probabilities of each detection and keep ones with score above a certain threshold (0.7)
2. For remaining boxes, a. Box with highest score is the detection results. b. Discard any remaining boxes with IoU > 0.5 with final detected box c. i.e. overlap with the box with highest score.

Anchor Boxes

• Associate each object to:
  • A cell which contains its mid-point and
  • Anchor box for the cell with highest IoU
• Calculate the IoU of Anchor boxes and prediected Bounding Boxes.
  • $IoU(P_{bb}, A_{bb}) = \frac{Area of Overlap}{Area of Union}$
• $\hat{y} = \{P_0, x, y, h, w, C1, C2, \quad P_0 x, y, h, w, C1, C2\}$
  • $P_0$ is objectness score
  • $x, y$ are the coordinates of the center of the bounding box relative to
  • $h, w$ are the height and width of the bounding box
  • $C1, C2$ are the class information for the object in the bounding box

YOLO

• Real-time performance with 45 FPS, 0.02 sec per image
• Not suitable for small objects
• Issues with new or multiple aspect ratios and unable to generalize

SSD, Single Shot Detector

• Similar to YOLO, VGG16 base Convolutional Neural Network layers
• Take advantage of Anchor boxes with different aspect ratios
• Large number of anchors boxes are chosen
• Not suitable for small objects
• 3 times faster than Faster R-CNN
• with ResNet-101 base SSD may help in detecting small objects with better features from the CONV layers

Overview of Object Detection

• Base Networks
  • VGG156
  • ResNet-101
  • Inception-v2, v3
  • ResNet
  • MobileNet
  • Alexnet
  • ZFNet
• Object Detection Framework
  • R-CNN family
  • YOLO family
  • SSD family
  • F-RCNN family
• Faster-RCNN is more accurate but slower
• YOLO/SSD are faster/real-time but may not be very accurate