紧跟着2D Detection的pipeline,3D Detection的演变(发展)也是从two-stage detector到one-stage detector。关于2D detector的发展,可以从基于深度学习的目标检测算法综述和从RCNN到SSD,这应该是最全的一份目标检测算法盘点了解一下,来自合工大的综述性论文"Object Detection with Deep Learning A Review"也非常全面。 PIX...
有人提到one-stage detector仅相当于多分类的RPN，很准确，从模型的原理上讲就是这个样子，而one-stage...
6、RetinaNet(2017) 提出Single stage detector不好的原因完全在于: 极度不平衡的正负样本比例:anchor近似于sliding window的方式会使正负样本接近1000:1,而且绝大部分负样本都是easy example,这就导致下面一个问题: gradient被easy example dominant的问题:往往这些easy example虽然loss很低,但由于数 量众多,对于loss依...
One stage detector (RetinaNet)-based crack detection for asphalt pavements considering pavement distresses and surface objectsPavement distressAutomated distress detectionCrack detectionDeep learningFaster R-CNNRetinaNetIn this study, a supervised machine learning network model is proposed to detect and ...
SSD,全称Single Shot MultiBox Detector,是Wei Liu在ECCV 2016上提出的一种目标检测算法,Wei Liu2009年本科就读于南京大学本科,后来是北卡罗莱娜大学博士。ECCV的全称是European Conference on Computer Vision(欧洲计算机视觉国际会议) ,两年一次,是计算机视觉三大会议(另外两个是ICCV和CVPR)之一。每 卷积 损失函数 ...
目标检测中One-stage检测算法 -> SSD,SSD,全称SingleShotMultiBoxDetector,是WeiLiu在ECCV2016上提出的一种目标检测算法,WeiLiu2009年本科就读于南京大学本科,后来是北卡罗莱娜大学博士。ECCV的全称是EuropeanConferenceonComputerVision(欧洲计算机视觉国际会议),
This paper first explores non-aligned visibleinfrared object detection with complex deviations in translation, scaling, and rotation, and proposes a fast one-stage detector YOLO-Adaptor, which introduces a lightweight multi-modal adaptor to simultaneously predict alignment parameters and confidence weights...
In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre- defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we als...
有人提到one-stage detector仅相当于多分类的RPN，很准确，从模型的原理上讲就是这个样子，而one-stage...
这周要在组内group reading细讲one-stage object detector,刚好在专栏系统的分享一下阅读心得,也希望看官指出不足或者理解有误的地方,本文主要是从论文novelty及代码实现着手,主要有以下四篇文章(持续更新): YOLO - Project: pjreddie.com/darknet/yo YOLOv2 - Paper: arxiv.org/abs/1612.0824 YOLOv3 - Paper:...