|
|
|
|
|
Hang Li, Shengjie Zhao and Hao Deng
The extraction of community-scale green infrastructure (CSGI) poses challenges due to limited training data and the diverse scales of the targets. In this paper, we reannotate a training dataset of CSGI and propose a three-stage transfer learning method ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ogbaje Andrew, Armando Apan, Dev Raj Paudyal and Kithsiri Perera
The accuracy of most SAR-based flood classification and segmentation derived from semi-automated algorithms is often limited due to complicated radar backscatter. However, deep learning techniques, now widely applied in image classifications, have demons...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yiwen Tang, Jiaxin Zhang, Runjiao Liu and Yunqin Li
Streets are an important component of urban landscapes and reflect the image, quality of life, and vitality of public spaces. With the help of the Google Cityscapes urban dataset and the DeepLab-v3 deep learning model, we segmented panoramic images to ob...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yifan Si, Dawei Gong, Yang Guo, Xinhua Zhu, Qiangsheng Huang, Julian Evans, Sailing He and Yaoran Sun
DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dim...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sanlong Jiang, Shaobo Li, Qiang Bai, Jing Yang, Yanming Miao and Leiyu Chen
A reasonable grasping strategy is a prerequisite for the successful grasping of a target, and it is also a basic condition for the wide application of robots. Presently, mainstream grippers on the market are divided into two-finger grippers and three-fin...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fangfang Liu and Ming Fang
Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biological ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ahram Song and Yongil Kim
Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficu...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jinglun Li, Jiapeng Xiu, Zhengqiu Yang and Chen Liu
Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation o...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chan Zeng, Junfeng Zheng and Jiangyun Li
The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection al...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chengming Zhang, Shuai Gao, Xiaoxia Yang, Feng Li, Maorui Yue, Yingjuan Han, Hui Zhao, Ya?nan Zhang and Keqi Fan
When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for ex...
ver más
|
|
|
|
|
|