(本文翻译自:SSD:Single Shot Detector) 摘要 我们提出仅用一个深层神经网络检测便可以在图像上检测目标的方法。我们的方法称作SSD,它把边界框的输出空间离散化成一系列的默认框(default box),这些默认框在特征图的每个位置有不同的宽高比和尺寸。在预测的时候,网络在每个默认框都为所有的类别生成了代表该类别存在的...
1. 我们提出了SSD,一个多分类单杆检测器(single-shot detector),比现在的单杆检测器(YOLO)更快,和那些较慢技术精度一样,并且超过了区域提议(region proposals)和池化(pooling)的方法,包括faster R-CNN. 2. SSD的核心部分是预测分类得分和一个固定集合的默认边界框的框偏移,这些是通过使用卷积滤波器到特征图上...
(Single-Shot Detector) [2] use a fully convolutional approach in which the network is able to find all objects within an image in one pass (hence ‘single-shot’ or ‘look once’) through the convnet. The region proposal algorithms usually have slightly better accuracy but slower to run,...
这样,总共会获得6组w和h值,即6个“特定尺寸矩形”。 2.6 “特定尺寸矩形”匹配规则 “特定尺寸矩形”锁定区域与实际目标加标的矩形区域是否匹配的规则是:两者的IoU值(详见Faster R-CNN中的2.3.1的b.)是否大于0.5,如果是,则定义为正匹配,否则定义为负匹配; 上图蓝色矩形框是实际目标加标的矩形区域,绿色矩形框...
In this project I have implemented Object Detection using a single shot detector. The tricky part was the objects were densely populated as the images were of a retail store. deep-neural-networkscomputer-visiontensorflowpython3object-detectionsingle-shot-detection ...
目标检测SSD: Single Shot MultiBox Detector 一、前言 1.1 什么是目标检测 目标检测问题可以分为以下两个问题: 分类:所有类别的概率 定位: 4个值(中心位置x,y,宽w,高h) 目标检测近年来已经取得了很重要的进展,主流的算法主要分为两个类型(参考RefineDet):(1)two-stage方法,如R-CNN系算法,其主要思路是先...
We summarize our contributions as follows: * We introduce SSD, a single-shot detector for multiple categories that is faster than the previous state-of-the-art for single shot detectors (YOLO), and significantly more accurate, in fact as accurate as slower techniques that perform explicit region...
DSSD : Deconvolutional Single Shot Detector 本文的主要贡献是将附加上下文引入到最先进的一般目标检测中。为了实现这一点,我们首先结合了一个最先进的分类器和一个快速检测框架。然后,我们使用反褶积层来增加SSD+Residual-101,以在目标检测中引入额外的大规模上下文,并提高准确性,特别是对于小目标,我们将生成的系统...
–We introduce SSD, a single-shot detector for multiple categories that is faster than the previous state-of-the-art for single shot detectors (YOLO), and significantly more accurate, in fact as accurate as slower techniques that perform explicit region proposals and pooling (including Faster R-...
个边界框(其中conv4_3, conv10_2, conv11_2的feature map上的每个位置预测4个default box;conv7, conv8_2, conv9_2预测6个)。 训练时,首先要匹配这些default boxes和ground truth boxes。 举例来说,上图b中的两个蓝色default box(即default box在原图上的投影)都match了ground truth中的猫,上图c中的红...