SSD: Single Shot Multibox Detector

NamHyuk Ahn

Object Detection

- mean Average Precision (mAP)

• Popular eval metric

• Compute average precisionfor single class, and average them over all classes

• Detections is True-positive if box is overlap with ground-truth more than some threshold (usually use 0.5)

Object Detection

- R-CNN Family• Most popular detection method in deep learning

• Use region proposal method <- make model slow

• Good accuracy (Faster: 73.2% mAP), but very slow

• R-CNN: 50 sec/img, Fast: 2 sec/img, Faster: 0.2 sec/img (7 FPS)

- YOLO (You Only Look Once)• Real-time (45 FPS), but low accuracy (63.4% mAP)

YOLO:You Only Look Once- Single shot detector model

• Not separate classification and bbox regression

- Divide image into S x S grid (7x7 in paper)• Within each grid cell, (4+1)*B + C vector,

• B: # of boxes in each grid (2 in paper)C: # of classes (20 in paper)(4+1): 4 box coord + 1 box confidence

- Direct prediction using CNN with regression loss

YOLO:You Only Look Once- Operate on a single-scale feature map (last pool)

• Bad accuracy with large or small object

- Predict bbox using fc layer

- Hard data augmentation, 448x448 input image

- Use customized CNN architecture

SSD:Single Shot Multibox Detector

- Multi-scale feature maps for detection • Add conv layer at the end of base network, decrease size progressively

• Concat output of multi-scale feature map at the last layer

- Convolutional predictors for detection• YOLO use fc layer, but SSD use 3x3 conv kernel

SSD:Single Shot Multibox Detector- Default boxes and aspect ratios

• Set default boxes at each location, and predict offset relative to corresponding default box

• output dims: (C+4)K*M*N, K=# of default box, C=# of classes, MN=feature dims

SSD:Single Shot Multibox Detector

- Default boxes and aspect ratios• Use 6 default boxes at each feature cell

• { 1, 2, 3, 1/2, 1/3 } aspect ratio boxes + 1 box with 1 aspect ratio

• Set 3 boxes in conv4_3 to reduce computation

SSD:Single Shot Multibox Detector- Output feature (final layer)

• With given output boxes from multi-scale features, sort them using class confidence

• Pick top-200 boxes and make each box 7-dim vector

• [ batch_idx, class_confidence, label, box offset…]

• Output feature dim is 7x200

•

Model analysis- Data argumentation is very important

- More feature map is better• Lower feature map can capture fine-grained details of object

- More default box shape is better• If you only 4 boxes, performance drop by 0.9%

• Using variety shape of default box makes predicting box easier

- Astrous VGG is better and faster

Result

- Accuracy is compare to state-of-the-art, and with real-time

Reference

- Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." arXiv preprint arXiv:1512.02325 (2015).