Without bells and whistles, our BBFE improves different baseline methods (both anchor-based and anchor-free) by a large margin ( [Math Processing Error] \\sim 2.0 points higher AP) on COCO, surpassing common fe
In most of the object detection tasks, the low-level feature maps with high resolution contain more location and detail information. However, the low-level feature maps lack semantic information. The high-level feature maps have more semantic information based on the convolution operator. The local...
论文阅读《Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection》 Tywwhale 1 人赞同了该文章 摘要 无人机(UAV)图像的目标检测是近年来研究的热点。现有的目标检测方法在一般场景上取得了很好的结果,但无人机图像存在固有的挑战。无人机图像的检测精度受到复杂背景、显著尺度差异...
在计算机视觉中,识别不同尺度的物体是一个基本的挑战。构建在图像金字塔之上的特征金字塔(简而言之,我们称这些特征金字塔为featurized image pyramid)构成了标准解决方案的基础,如下图所示(a)所示。这些金字塔是尺度不变的,因为一个物体的尺度变化是通过改变其在金字塔中的层级来抵消的。直观地说,这个属性使模型能够通过...
While statement returning Application / Object defined error "Error 1004" I am currently creating a macro that should be able to read through a set of data that looks like the below image and create new sheets based on the first 3 digits of the account number. For example: ......
Rich feature hierarchies for accurate object detection and semantic segmentation 一、摘要 在PASCAL VOC标准数据集上测量的目标检测性能在最近几年趋于稳定。性能最好的方法是复杂的集成系统,它通常将多个低层图像特性与高层上下文结合起来。在本文中,我们提出了一种简单、可扩展的检测算法,相对于之前VOC 2012的最佳...
Feature pyramid network is widely used in advanced object detection. By simply changing the network connection, the performance of small object detection can be greatly improved without increasing th...
与其他强大的分类器(例如,Rotation-based SVM,结构化的SVM,旋转森林),增强型的迭代选择弱学习者从候选人弱分类器从上一轮努力处理的例子,它们一边被视为一个增强模型集成前成绩,贪婪地最小化一个指数损失函数。每一个弱学习者都能对样本进行重估,然后弱学习者会更关注那些被前一个学习者错误分类的例子。利用该...
FPN来源于论文《Feature Pyramid Networks for Object Detection》 1.1要解决的问题 传统的物体检测模型通常只在深度卷积网络的最后一个特征图上进行后续操作,而这一层对应的下采样率(图像缩小的倍数)通常又比较大,如16、32,造成小物体在特征图上的有效信息较少,小物体的检测性能会急剧下降,这个问题也被称为多尺度问...
A sample application is provided so interested readers can try the image detection and see how it can be performed using the framework. Background The contributions brought by Paul Viola and Michael Jones were threefold. First, they focused on creating a classifier based on the combination of ...