市面上主流的目标检测算法框架为:faster RCNN, Yolo系列, FCOS, centerNet 等,今天先介绍anchor base的边框回归,为方便书写,以下将boundingbox regression简写成BBR,gt为groundtruth 真实框,grid为网格,为feature map返回到输入网络图片的感受野大小。 anchor based 目标检测,因为有anchor锚框的存在,在回归中扮演了举足...
MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression MPDIoU:一个有效和准确的边界框损失回归函数 摘要 边界框回归(Bounding box regression, BBR)广泛应用于目标检测和实例分割,是目标定位的重要步骤。然而,当预测框与边界框具有相同的纵横比,但宽度和高度值完全不同时,大多数现有的边界框回归...
技术标签:BoundingBoxRegressionBBR边框回归RCNN 为什么要做BoundingBox Regression(BBR)? 首先我们先来考虑,RCNN中为什么要做BoundingBox-Regression? Bounding Boxregression是 RCNN中使用的边框回归方法,在RCNN的论文中,作者指出:主要的错误是源于mislocalization。为了解决这个问题,作者使用了bounding box regression。 这...
MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression MPDIoU:一个有效和准确的边界框损失回归函数 摘要 边界框回归(Bounding box regression, BBR)广泛应用于目标检测和实例分割,是目标定位的重要步骤。然而,当预测框与边界框具有相同的纵横比,但宽度和高度值完全不同时,大多数现有的边界框回归损失...
最近看了RNN的文章,对里面的Bounding-box regression回归不甚理解,google一番,把学到的东西写在这里。 参考的文章。 为啥要回归 鉴于bounding box太长,下面简写为bb,bounding box regression 简写为bbr。 首先,原始的bb是用selective research选出来的...
To this end, we propose a class-agnostic and anchor-free box regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a bounding box of the nearest object from any given box. Trained on a relatively small set of annotated images, UBBR successfully generalizes to unseen ...
? 2023 Elsevier LtdBounding box regression (BBR) is one of the core tasks in object detection, and the BBR loss function significantly impacts its performance. However, we have observed that existing IoU-based loss functions suffer from unreasonable penalty factors, leading to anchor boxes expanding...
预测器采取一个多实例学习分支(MLP: Multi-Instance-Learning)和检测框回归分支(BBR: Bounding Box Regression)组成,总损失函数为: wsddn 为 MIL分支预测的分数交叉熵损失;r 为经过优化层后的类别交叉熵损失;作者还额外加了一层 RPN 结构帮助补充检测框,rpn-cls 为 RPN 结构输出的预测类别交叉熵损失;rpn-det 为...
In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both $\ell_n$-norm and IOU-based loss functions are inefficient to depict the obje...
摘要: In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both ℓn-norm and IOU-based loss functions are inefficient to depict the ob...