Results 21 to 30 of about 18,989 (190)

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

PLoS ONE, 2021
This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images.
Wentong Wu, Han Liu, Lingling Li, Yilin Long, Xiaodong Wang, Zhuohua Wang, Jinglun Li, Yi Chang   +7 more
doaj   +2 more sources

A Semantic Segmentation Method for Buffer Layer Defect Detection in High Voltage Cable [PDF]

E3S Web of Conferences, 2021
A semantic segmentation method based on the fully convolutional network is proposed to detect the buffer layer defect in high voltage cable automatically. One hundred seventy-seven high-resolution X-ray images of cables are collected.
Jun Zhang, Xiaoli Duan, Yi Xie, Jianjia Duan, Fuyong Huang, Zeyu Zeng   +5 more
doaj   +1 more source

Domain‐adapted driving scene understanding with uncertainty‐aware and diversified generative adversarial networks

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel.
Yining Hua, Jie Sui, Hui Fang, Chuan Hu, Dewei Yi   +4 more
wiley   +1 more source

A Comparative Study of Five Networks for Reservoir Classification Based on Geophysical Logging Signals

IEEE Access, 2020
Unconventional reservoir classification suffers low accuracy because of the complex geophysical properties. With good performance and moderate cost, geophysical logging is considered to be of great potential as the compromise solution between seismic and
Kai Zhu, Yonghui Du, Qian Wang, Naihua Ji, Li Zhang   +4 more
doaj   +1 more source

Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor

Frontiers in Oncology, 2021
ObjectivesTo automate image delineation of tissues and organs in oncological radiotherapy by combining the deep learning methods of fully convolutional network (FCN) and atrous convolution (AC).MethodsA total of 120 sets of chest CT images of patients ...
Hui Xie, Hui Xie, Jian-Fang Zhang, Qing Li, Qing Li   +4 more
doaj   +1 more source

A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Convolutional neural network (CNN) has achieved remarkable success in polarimetric synthetic aperture radar (PolSAR) image classification. However, the PolSAR image classification is a pixelwise prediction assignment.
Feng Zhao, Min Tian, Wen Xie, Hanqiang Liu   +3 more
doaj   +1 more source

A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds

Remote Sensing, 2022
The accurate separation between leaf and woody components from terrestrial laser scanning (TLS) data is vital for the estimation of leaf area index (LAI) and wood area index (WAI).
Tao Han, Gerardo Arturo Sánchez-Azofeifa   +1 more
doaj   +1 more source

Time series classification based on statistical features

EURASIP Journal on Wireless Communications and Networking, 2020
This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC.
Yuxia Lei, Zhongqiang Wu
doaj   +1 more source

LSTM Fully Convolutional Networks for Time Series Classification

IEEE Access, 2018
Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences.
Fazle Karim, Somshubra Majumdar, Houshang Darabi, Shun Chen   +3 more
doaj   +1 more source

Patch-Based Training of Fully Convolutional Network for Hyperspectral Image Classification With Sparse Point Labels

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Fully convolutional network (FCN), which has excellent capability for capturing spatial context, was introduced to improve the performance of hyperspectral image classification (HSIC).
Xueliang Zhang, Zixian Zheng, Pengfeng Xiao, Zhenshi Li, Guangjun He   +4 more
doaj   +1 more source