Results 1 to 10 of about 6,343,053 (206)

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Sensors
Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by ...
Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin, Anton Satria Prabuwono   +3 more
doaj   +3 more sources

Fault Detection Based on Fully Convolutional Networks (FCN)

Journal of Marine Science and Engineering, 2021
It is of great significance to detect faults correctly in continental sandstone reservoirs in the east of China to understand the distribution of remaining structural reservoirs for more efficient development operation.
Jizhong Wu, Bo Liu, Hao Zhang, Shumei He, Qianqian Yang   +4 more
doaj   +4 more sources

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

Remote Sensing, 2018
Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem.
Yangyang Li, Yanqiao Chen, Guangyuan Liu, Licheng Jiao   +3 more
doaj   +4 more sources

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

Remote Sensing, 2022
Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is ...
Wen Xie, Licheng Jiao, Wenqiang Hua
doaj   +3 more sources

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   +3 more sources

FCN-SFW: Steel Structure Crack Segmentation Using a Fully Convolutional Network and Structured Forests

IEEE Access, 2020
Tiny cracks that exist in steel beams have poor continuity and low contrast in images, posing a huge challenge to crack detection using image-based approaches.
Sen Wang, Yunlong Pan, Mingfang Chen, Yinhui Zhang, Xing Wu   +4 more
doaj   +2 more sources

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   +3 more sources

Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Conventional remote sensing image retrieval (RSIR) system usually performs single-label retrieval where each image is annotated by a single label representing the most significant semantic content of the image.
Zhenfeng Shao, Weixun Zhou, Xueqing Deng, Maoding Zhang, Qimin Cheng   +4 more
doaj   +2 more sources

FCN Attention Enhancing Asphalt Pavement Crack Detection Through Attention Mechanisms and Fully Convolutional Networks

Scientific Reports
This paper presents an innovative approach to detecting cracks in asphalt pavement using an FCN-attention model, which integrates attention mechanisms into a fully convolutional network (FCN) for enhanced pixel-level segmentation.
Guoping Hu
exaly   +2 more sources

From Local to Global: Class Feature Fused Fully Convolutional Network for Hyperspectral Image Classification

Remote Sensing, 2021
Current mainstream networks for hyperspectral image (HSI) classification employ image patches as inputs for feature extraction. Spatial information extraction is limited by the size of inputs, which makes networks unable to perform effective learning and
Qian Liu, Zebin Wu, Xiuping Jia, Yang Xu, Zhihui Wei   +4 more
doaj   +2 more sources