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Mobilecracknet:A Real-Time Crack Segmentation Network

https://doi.org/10.2139/ssrn.4785137
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/stc.2551openalex-extracted
CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-extracted
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s11263-021-01515-2openalex-extracted
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.1016/j.conbuildmat.2022.127137openalex-extracted
Crack detection for nuclear containments based on multi-feature fused semantic segmentation
inferredcites →https://doi.org/10.1016/j.conbuildmat.2023.132839openalex-extracted
Advanced crack detection and segmentation on bridge decks using deep learning
inferredcites →https://doi.org/10.1016/j.neucom.2019.01.036openalex-extracted
DeepCrack: A deep hierarchical feature learning architecture for crack segmentation
inferredcites →https://doi.org/10.1016/j.neucom.2023.02.025openalex-extracted
BiSeNet V3: Bilateral segmentation network with coordinate attention for real-time semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-extracted
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-extracted
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-extracted
Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2018.00745openalex-extracted
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-extracted
Non-local Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-extracted
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/tie.2019.2945265openalex-extracted
SDDNet: Real-Time Crack Segmentation
inferredcites →https://doi.org/10.1109/tip.2020.3042065openalex-extracted
CGNet: A Light-Weight Context Guided Network for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tits.2021.3095507openalex-extracted
CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network
inferredcites →https://doi.org/10.1109/tnnls.2022.3176493openalex-extracted
LMFFNet: A Well-Balanced Lightweight Network for Fast and Accurate Semantic Segmentation
inferredcites →https://doi.org/10.1109/tpami.1986.4767851openalex-extracted
A Computational Approach to Edge Detection
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-extracted
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1145/3338533.3366558openalex-extracted
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.23919/ccc55666.2022.9902015openalex-extracted
Real-time Semantic Segmentation for Aggregating Long-Range Information and Region Understanding
inferredcites →https://doi.org/10.4103/0256-4602.62225openalex-extracted
Automatic Pavement Crack Detection Using Texture and Shape Descriptors
inferredcites →https://doi.org/10.48550/arxiv.1411.4038openalex-extracted
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1606.02147openalex-extracted
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-extracted
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1709.01507openalex-extracted
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.48550/arxiv.1907.11357openalex-extracted
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.2010.11929openalex-extracted
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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