faster rcnn
时间: 2023-09-13 20:12:06 浏览: 53
Faster R-CNN (Region-based Convolutional Neural Network) is a popular object detection algorithm developed by researchers at Microsoft Research Asia. It is an improvement over the previous R-CNN and Fast R-CNN models and achieves state-of-the-art results on various benchmark datasets.
Faster R-CNN consists of two main components: a Region Proposal Network (RPN) and a Fast R-CNN network. The RPN generates candidate object regions and proposes them to the Fast R-CNN network, which classifies and refines the proposed regions.
The RPN is based on a fully convolutional network (FCN) that slides over the entire image and predicts objectness scores and bounding box regressions at each position. The proposals are ranked by their objectness scores and non-maximum suppression is applied to remove overlapping proposals.
The Fast R-CNN network takes the proposed regions from the RPN and extracts a fixed-length feature vector for each region using RoI (Region of Interest) pooling. These feature vectors are then fed into fully connected layers for classification and bounding box regression.
Overall, Faster R-CNN achieves high accuracy and efficiency in object detection tasks and has become a popular choice in both academia and industry.
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