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Object Detection Methods using Deep Learning
The document discusses object detection techniques including R-CNN, SPPnet, Fast R-CNN, and Faster R-CNN. R-CNN uses region proposals and CNN features to classify each region. SPPnet improves efficiency by computing CNN features once for the whole image. Fast R-CNN further improves efficiency by sharing computation and using a RoI pooling layer. Faster R-CNN introduces a region proposal network to generate proposals, achieving end-to-end training. The techniques showed improved accuracy and processing speed over prior methods.
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- 58. R-CNN 58 Image Regions ResizeConvolution Features Classify
- 59. SPP net 59 Image ConvolutionFeatures SPPRegions Classify
- 60. R-CNN vs. SPPnet 60 R-CNN SPP net
- 61. Fast R-CNN 61 Image Convolution Features Regions RoIPooling Layer Class Label Confidence RoI Pooling Layer Class Label Confidence
- 62. R-CNN vs. SPPnet vs. Fast R-CNN 62 R-CNN SPP net Fast R-CNN
- 63. Faster R-CNN 63 Image FullyConvolutional Features Bounding Box Regression BB Classification FastR-CNN
- 64. R-CNN vs. SPPnet vs. Fast R-CNN 64 R-CNN SPP net Fast R-CNN Faster R-CNN
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