whz1861的博客

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection net

主要贡献提出了 RPN(Region Proposal Networks) 网络来计算候选框 主要步骤:特征提取: 同 Fast R-CNN，以整张图为输入，利用 CNN 得到图像的特征层区域提名: 在最终的卷积特征层conv5-3上利用K 个不同的矩形框 (Anchor Box) 进行提名， k 一般取 9分类与回归: 对每一个 Anchor Box 对应的区域： 进行 object

Feature pyramids are as basic component in recognition systems for detecting objects at different scales. in this paper, we exploit the inherent multi-scale, pyramids hierarchy of deep convolutiona

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality s