首页Faster R-CNN算法介绍

Faster R-CNN算法介绍

时间: 2023-12-23 11:02:21 浏览: 93

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是一种流行的目标检测算法，它由R-CNN、Fast R-CNN和Faster R-CNN三个阶段组成。 R-CNN首先使用选择性搜索算法提取图像中的候选区域，然后对每个候选区域进行卷积操作，并使用支持向量机（SVM）进行分类。由于每个候选区域都需要单独进行卷积操作，因此R-CNN的速度非常慢。 Fast R-CNN改进了R-CNN的效率，它将整个图像输入到一个卷积神经网络（CNN）中，然后使用RoI池化层将每个候选区域映射到CNN的特征图上。最后，Fast R-CNN使用全连接层和softmax分类器对每个候选区域进行分类。这种方法比R-CNN快得多，但是选择性搜索算法仍然是瓶颈。 Faster R-CNN通过引入一个称为Region Proposal Network（RPN）的网络来解决选择性搜索算法的瓶颈问题。RPN是一个小型CNN，它可以在特征图上生成候选区域，这些候选区域可以直接输入到RoI池化层中进行分类和回归。Faster R-CNN比Fast R-CNN更快，同时具有更好的准确性。总的来说，Faster R-CNN是一个高效且准确的目标检测算法，它已经被广泛应用于图像分析和计算机视觉领域。

阅读全文