faster rcnn
时间: 2023-09-24 07:08:01 浏览: 89
Faster R-CNN is a deep learning architecture for object detection that was introduced in 2015 by researchers at Microsoft Research Asia. It is an extension of the popular Region-based Convolutional Neural Network (R-CNN) approach, which was introduced in 2014.
Faster R-CNN uses a two-stage approach for object detection. In the first stage, a region proposal network (RPN) is used to generate potential object bounding boxes. The RPN is a fully convolutional network that takes an image as input and outputs a set of object proposals, each of which is a rectangular bounding box. These proposals are then passed to the second stage, which is a Fast R-CNN network that classifies each proposal and refines its bounding box.
The key innovation of Faster R-CNN is the use of the RPN to generate region proposals, which eliminates the need for external region proposal algorithms such as Selective Search. This makes the architecture faster and more accurate than previous methods. Additionally, the RPN can be trained end-to-end with the Fast R-CNN network, resulting in better performance.
Faster R-CNN has been widely used for object detection tasks in various domains, including self-driving cars, robotics, and surveillance. It has achieved state-of-the-art results on several benchmark datasets, including PASCAL VOC and COCO.