目标检测--Single-Shot Refinement Neural Network for Object Detection
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Single-Shot Refinement Neural Network for Object Detection
https://github.com/sfzhang15/RefineDet
针对目标检测,本文可以看作将 Faster RCNN 和 SSD 融合起来。
1 Introduction
当前基于 CNN 网络的目标检测可以分为两大类:1) the two-stage approach,2)the one-stage approach
1) the two-stage approach
首先是候选区域的提取,然后是目标的分类和回归,这类方法的检测精度要好于the one-stage approach
2)the one-stage approach
这类方法是通过 regular and dense sampling over locations, scales and aspect ratios,速度快,精度稍微差些,主要原因是 the class imbalance problem,即正负样本比例严重失调
本文提出一个目标检测框架 RefineDet, to inherit the merits of the two approaches (i.e., one-stage and two-stage approaches) and overcome their shortcomings
Architecture of RefineDet
网络主要包括两个相互关联的模型 Anchor Refinement Module (ARM)和 Object Detection Module(ODM),这两个模块通过 transfer connection block (TCB) 联系起来。
Anchor Refinement Module (ARM) 可以看作一个简化的 SSD,这里只做二分类,即目标的有无,去除一些无物体的候选区域,对位置和尺寸进行大致的调整,为后面的 ODM 提高一个好的初始化
ARM aims to remove negative anchors so as to reduce search space for the classifier and also coarsely adjust the locations and sizes of anchors
to provide better initialization for the subsequent regressor
Object Detection Module(ODM) 可以看作为一个 Fast RCNN, 多类别分类是和矩形框回归
ODM aims to regress accurate object locations and predict multi-class labels based on the refined anchors
Transfer Connection Block (TCB) 这个模块可以看作 FCN 中的 deconvolution layers, 将不同网络层的特征融合起来
5 Experiments
RefineDet320+ 、RefineDet512+ : multi-scale testing strategy
11
- 目标检测--Single-Shot Refinement Neural Network for Object Detection
- 目标检测--A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
- 目标检测“A MultiPath Network for Object Detection”
- 论文笔记:Inception Single Shot MultiBox Detector for object detection
- 目标检测--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 目标检测--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 无人驾驶中的目标检测--MODNet: Moving Object Detection Network for Autonomous Driving
- 目标检测方法简介:RPN(Region Proposal Network) and SSD(Single Shot MultiBox Detector)
- 目标检测--SSD: Single Shot MultiBox Detector
- [目标检测]SSD: Single Shot MultiBox Detector
- 目标检测--Feature Pyramid Networks for Object Detection
- 目标检测“Feature Pyramid Networks for Object Detection”
- 目标检测--Focal Loss for Dense Object Detection
- 目标检测“Perceptual Generative Adversarial Networks for Small Object Detection”
- 目标检测“Focal Loss for Dense Object Detection”
- 目标检测“Perceptual Generative Adversarial Networks for Small Object Detection”
- 目标检测“Perceptual Generative Adversarial Networks for Small Object Detection”
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