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22/7/2019, · Unlike ,R,-,CNN,, Fast ,R,-,CNN, uses a single deep ConvNet to extract features for the entire image once. We also create a set of ROI(Region of Interest) for the image using selective search. Region of interest (RoI) layer extracts a fixed-length feature vector from the feature map for each object proposal for object detection .

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10/7/2018, · Therefore, algorithms like ,R,-,CNN,, ,YOLO, etc have been developed to find these occurrences and find them fast. ,R,-,CNN, To bypass the problem of selecting a huge number of regions, Ross Girshick et al . proposed a method where we use selective search to extract just 2000 regions from the image and he called them region proposals.

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10/8/2018, · This post talks about ,YOLO, and Faster-RCNN. These are the two popular approaches for doing object detection that are anchor based. Faster RCNN offers a regional of interest region for doing convolution while ,YOLO, does detection and classification at the same time. I would say that ,YOLO, appears to be a cleaner way of doing object detection since it’s fully end-to-end training.

The basic idea and difference to other object detectors, such as, Faster ,R,-,CNN, [33], ,Mask R,-,CNN, [34], SSD [35], etc. of "You Only Look Once" (,YOLO,) [36] method is that it uses a single-stage ...