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 +4 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 [PDF]
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 +3 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 +8 more
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MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 Accepted in CVPR ...
Hao Yang +3 more
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R-FCN: Object Detection via Region-based Fully Convolutional Networks [PDF]
, 2016 We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
Jifeng Dai, Yi Li, Kaiming He, Jian Sun
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FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation [PDF]
2020 International Joint Conference on Neural Networks (IJCNN), 2020 7 pages, 6 figures, Accepted at IJCNN 2020: International Joint Conference on Neural ...
Celso A. M. Lopes +4 more
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Feature extraction from satellite images using segnet and fully convolutional networks (FCN)
International Journal of Engineering and Geosciences, 2020 Object detection and classification are among the most popular topics in Photogrammetry and Remote Sensing studies. With technological developments, a large number of high-resolution satellite images have been obtained and it has become possible to distinguish many different objects.
Batuhan Sarıtürk +3 more
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FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic Segmentation of Brain Anatomy [PDF]
, 2019 In this paper, a 3D patch-based fully dense and fully convolutional network (FD-FCN) is proposed for fast and accurate segmentation of subcortical structures in T1-weighted magnetic resonance images. Developed from the seminal FCN with an end-to-end learning-based approach and constructed by newly designed dense blocks including a dense fully-connected
Binbin Yang, Weiwei Zhang
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