Fault Detection Based on Fully Convolutional Networks (FCN) [PDF]
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 +4 more
doaj +5 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 +3 more
doaj +4 more sources
Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation [PDF]
Visual Computer, 2018 Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis in computer-aided diagnosis systems. In recent years, segmentation methods based on fully convolutional networks (FCNs) have achieved great success in general images.
Jinman Kim, Lei Bi, Dagan Feng
exaly +7 more sources
FCN attention enhancing asphalt pavement crack detection through attention mechanisms and fully convolutional networks. [PDF]
Sci RepThis 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. The model employs a ResNet-50-based encoder and incorporates channel-wise and spatial attention modules to refine feature ...
Zhang H, Liu J, Hu G.
europepmc +4 more sources
FCN-SFW: Steel Structure Crack Segmentation Using a Fully Convolutional Network and Structured Forests [PDF]
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 +4 more
doaj +3 more sources
ME-FCN: A Multi-Scale Feature-Enhanced Fully Convolutional Network for Building Footprint Extraction
Remote SensingThe precise extraction of building footprints using remote sensing technology is increasingly critical for urban planning and development amid growing urbanization.
Hui Sheng +5 more
doaj +2 more sources
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 +7 more
doaj +2 more sources
LatLRR-FCNs: Latent Low-Rank Representation With Fully Convolutional Networks for Medical Image Fusion [PDF]
Frontiers in Neuroscience, 2021 Medical image fusion, which aims to derive complementary information from multi-modality medical images, plays an important role in many clinical applications, such as medical diagnostics and treatment. We propose the LatLRR-FCNs, which is a hybrid medical image fusion framework consisting of the latent low-rank representation (LatLRR) and the fully ...
Zhengyuan Xu +9 more
openaire +3 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 +5 more
doaj +1 more source
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 +4 more
doaj +1 more source