目标检测(Object Detection) 一、基本概念 1. 什么是目标检测 目标检测(Object Detection)的任务是找出图像中所有感兴趣的目标(物体),确定它们的类别和位置,是计算机视觉领域的核心问题之一。由于各类物体有不同的外观、形状和姿态,加上成像时光照、遮挡等因素的干扰,目标检测一直是计算机视觉领域最具有挑战性的问题。
在开发技术细节之前,我们注意到由于R-CNN在region上运行,因此很自然将其扩展到语义分割的任务。稍加修改,我们在PASCAL VOC分割任务上也取得了竞争性结果,在VOC 2011测试集上平均分割精度为47.9% 2.Object detection with R-CNN 我们的目标检测系统由三个模块组成:第一个模块生成与类别无关的region proposals候选区,...
PASCAL VOC从2005年开始举办挑战赛,每年的内容都有所不同,从最开始的分类,到后面逐渐增加检测,分割,人体布局,动作识别(Object Classification 、Object Detection、Object Segmentation、Human Layout、Action Classification)等内容,数据集的容量以及种类也在不断的增加和改善。该项挑战赛催生出了一大批优秀的计算机视觉模...
We answer this question by bridging the gap between image classification and object detection. This paper is the first to show that a CNN can lead to dramatically higher object detection performance on PASCAL VOC as compared to systems based on simpler HOG-like features. To achieve this ...
作者提到花费在region propasals和提取特征的时间是13s/张-GPU和53s/张-CPU。r-cnn有点麻烦,他要先过一次classification得到分类的model,继而在得到的model上进行适当的改变又得到了detection的model,最后才开始在detection model cnn上进行边界检测。reference...
R-CNNs for Object Detection were first presented in 2014 by Ross Girshick et al., and were shown to outperform previous state-of-the-art approaches on one of the major object recognition challenges in the field: Pascal VOC. Since then, two follow-up papers were published which contain ...
Previous research in convertingconvolutional neural networks (CNNs)from 32-bit floating-point arithmetic (FP32) to 8-bit integer (INT8) for classification tasks is well understood. However, no solid work exists regarding accelerating CNN-based object detection task using INT8. We start withYOLO-...
[G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR' 16] |[pdf] [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR' 16] |[pdf] [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR' 16...
Today, deep CNN-based object detection algorithms are more and more used in Artificial Intelligence (AI) applications. However , it still very difficult to deploy large CNNs architectures on small devices with limited hardware resources, because they consist of millions of parameters, which make ...
A Multistage Framework for Detection of Very Small Objects Mahmut KarakayaDuleep Rathgamage DonRamazan S. Aygun Mar 2023 Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, an...