ImageNet是一个由很多研究人员和大学的贡献组成的开放数据集,用于研究各种类型的图像分类任务。它包含了多种类型的图像,例如花卉、动物、建筑物等等,每种类型的图像都有自己的子集,用于训练和测试各种不同的模型。ImageNet数据集始于2009年,由李飞飞教授等人发起,经过多年的发展,现在已经成为了研究图像分类的重要平台之一。
In the second step, using template matching of image stitching technique to expand the tool images into panorama images. To detect the region of tool wear area more efficiently, deep learning-based object detection and segmentation techniques, instead of traditional computer vision methods, ...
Tensorflow object detection API 搭建物体识别模型(二) 二、数据准备 1)下载图片 图片来源于ImageNet中的鲤鱼分类,下载地址:https://pan.baidu.com/s/1Ry0ywIXVInGxeHi3uu608g 提取码: wib3 在桌面新建文件夹目标检测,把下载好的压缩文件n01440764.tar放到其中,并解压 2)选择图片 在此数据集中,大部分图片...
在Object Detection过程中,不包含Object的Grid Cell往往比包含Object的Grid Cell要多很多,使得它们的Confidence Loss的贡献要大于包含Object的Grid Cell,为了解决这个问题,降低不包含Object的Grid Cell的的影响,设置两个权重常数,\lambda_{obj}=5,\lambda_{noobj}=0.5。原文中描述如下: in every image manygrid cells...
Accurate, fast and lightweight dense target detection methods are highly important for precision agriculture. To detect dense apricot flowers using drones, we propose an improved dense target detection method based on YOLOv8, named D-YOLOv8. First, we in
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results Dawei Du1, Pengfei Zhu2, Longyin Wen3, Xiao Bian4, Haibin Ling5, Qinghua Hu1, Tao Peng2, Jiayu Zheng2, Xinyao Wang3, Yue Zhang3, Liefeng Bo3, Hailin Shi...
[2017 ICCV] Focal Loss for Dense Object Detection Focal Loss主要解决的类别不平衡的问题,相比于Online Hard Sample Mining,这种loss更加灵活,具体的loss设置上,是在普通的CE Loss上增加一个调节因子,这样效果越好的时候,loss就越小。 后续的很多anchor-free的方法都是得益于focal loss,才能很好的被实现。 [2017...
This chapter explains how to use object detection based on deep learning. With object detection we want to find the different instances in an image and assign them to a class. The instances can partially overlap and still be distinguished as distinct. This is illustrated in the following schema...
WU Han,ZHANG Zhi-long,LI Chu-wei,LI Hang-yu.Infrared image sample amplification and object detection method with small samples[J].Control Theory & Applications,2021,38(9):1477~1485.[点击复制]小样本红外图像的样本扩增与目标检测算法 Infrared image sample amplification and object detection method wit...
所以我们首先在ImageNet 1000-Class数据集上预训上图中的前20层卷积层 + Average-Pooling Layer + Fully Connected Layer,在经过一周的训练后,在ImageNet 2012验证集上得到了88%的准确率。然后在预训练的神经网络基础上增加4个卷积层和2个随机初始化权重的全连接层。Detection需要丰富的视觉信息,所以我们将网络的...