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Faste R-CNN

时间: 2023-10-26 22:07:00 浏览: 120

Faster R-CNN

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate highquality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolutional features. For the very deep VGG-16 model [19], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2% mAP) and 2012 (70.4% mAP) using 300 proposals per image. Code is available at https://github.com/ShaoqingRen/faster_rcnn.

Faster R-CNN是一种目标检测算法，它是在R-CNN和Fast R-CNN的基础上发展而来的。相比于前两者，Faster R-CNN的速度更快，准确率更高。 Faster R-CNN的核心思想是引入了一个Region Proposal Network（RPN），用于生成候选框。RPN是一个全卷积网络，它可以在输入图像上滑动一个小窗口，并输出每个窗口是否包含物体以及物体的边界框。这些边界框被送入RoI Pooling层，然后被分类器和回归器共同处理。相比于Fast R-CNN，Faster R-CNN的速度提升了很多，因为它不需要在每个候选框上运行卷积神经网络。此外，Faster R-CNN的准确率也有所提高，因为它可以生成更加准确的候选框。

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