SSD: Single Shot MultiBox Detector

SSD is the extension or updating version of YOLO.

Compared with YOLO:
1 SSD use the multi-scale feature maps for detection, but the YOLO only use one scale for detection.

2 Before the decision layer, the YOLO use the fully connected layer for detection, but the SSD use the convolution filter for detection, which can improve the final detection performance.

3 Default boxes and aspect ratios. there are a set of default bounding boxes with each feature map cell for multiple feature maps at the top of the network.

R-CNN: Selective Search Region Proposals; SVM classification
Fast R-CNN: Selective Search Region Proposals; Shared computation by using SSP.
Faster R-CNN: Region Proposal Network
OverFeat VS SSD: If the SSD only use one default box per location from the topmost feature map, the SSD would have similar architecture to OverFeat.
YOLO VS SSD: If use the whole topmost feature map and add a fully-connected layer for predictions instead of convolutional predictors, it's very similar with the YOLO.





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