Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet

[1]  Yiming Yan,et al.  CycleADC-Net: A crack segmentation method based on multi-scale feature fusion , 2022, Measurement.

[2]  K. Yuen,et al.  Crack fractal analysis of fractured polyethylene fiber reinforced alkali activated mortar under flexural load , 2022, Construction and Building Materials.

[3]  Weiwei Cai,et al.  LLAM-MDCNet for Detecting Remote Sensing Images of Dead Tree Clusters , 2022, Remote. Sens..

[4]  Weiwei Cai,et al.  DS-MENet for the classification of citrus disease , 2022, Frontiers in Plant Science.

[5]  Weiwei Cai,et al.  A high-precision forest fire smoke detection approach based on ARGNet , 2022, Comput. Electron. Agric..

[6]  Weiwei Cai,et al.  BC-DUnet-based segmentation of fine cracks in bridges under a complex background , 2022, PloS one.

[7]  Linbing Wang,et al.  UNet-based model for crack detection integrating visual explanations , 2022, Construction and Building Materials.

[8]  Weiwei Cai,et al.  Fast forest fire smoke detection using MVMNet , 2022, Knowl. Based Syst..

[9]  T. Ma,et al.  Pavement distress detection using convolutional neural networks with images captured via UAV , 2022, Automation in Construction.

[10]  Parnia Shokri,et al.  Semantic Segmentation and 3D Reconstruction of Concrete Cracks , 2021, Remote. Sens..

[11]  Gang Li,et al.  Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network , 2021 .

[12]  Chenkai Shen,et al.  A real-time detection approach for bridge cracks based on YOLOv4-FPM , 2021 .

[13]  Xiaoning Cui,et al.  Intelligent crack detection based on attention mechanism in convolution neural network , 2021, Advances in Structural Engineering.

[14]  Danhui Dan,et al.  Automatic recognition of surface cracks in bridges based on 2D-APES and mobile machine vision , 2021 .

[15]  U. Nnolim.  Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection , 2020, Heliyon.

[16]  Yanyan Wang,et al.  RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks , 2020 .

[17]  Baijie Zhu,et al.  A research on an improved Unet-based concrete crack detection algorithm , 2020 .

[18]  Wei Lu,et al.  Image-based concrete crack detection in tunnels using deep fully convolutional networks , 2020 .

[19]  Lin Gao,et al.  Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features , 2020, Journal of Advanced Transportation.

[20]  Qingguo Tian,et al.  A fast adaptive crack detection algorithm based on a double-edge extraction operator of FSM , 2019, Construction and Building Materials.

[21]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Xiaochun Luo,et al.  Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network , 2018, Comput. Aided Civ. Infrastructure Eng..

[23]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[24]  Nhat-Duc Hoang,et al.  An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction , 2018, Advances in Civil Engineering.

[25]  A. Siahaan,et al.  Mobile Application Detection of Road Damage using Canny Algorithm , 2018, Journal of Physics: Conference Series.

[26]  Mohammad R. Jahanshahi,et al.  NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion , 2018, IEEE Transactions on Industrial Electronics.

[27]  Guiyuan Jiang,et al.  Automatic Pixel-Level Pavement Crack Detection Using Information of Multi-Scale Neighborhoods , 2018, IEEE Access.

[28]  Yunhong Wang,et al.  Receptive Field Block Net for Accurate and Fast Object Detection , 2017, ECCV.

[29]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[30]  Sumathi Poobal,et al.  Crack detection using image processing: A critical review and analysis , 2017, Alexandria Engineering Journal.

[31]  Fan Meng,et al.  Automatic Road Crack Detection Using Random Structured Forests , 2016, IEEE Transactions on Intelligent Transportation Systems.

[32]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[36]  Manuel J. C. Minhoto,et al.  Impact of Traffic Overload on Road Pavement Performance , 2013 .

[37]  Qingquan Li,et al.  CrackTree: Automatic crack detection from pavement images , 2012, Pattern Recognit. Lett..

[38]  Guang Hu,et al.  Conjunction of Active and Semi-Supervised Learning for Wireline Logs-Based Automatic Lithology Identification , 2022, IEEE Geoscience and Remote Sensing Letters.

[39]  A. Mottaeva,et al.  Development of transport infrastructure in modern megalopolises , 2018 .