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Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection [article]

Fan Yang, Lei Zhang, Sijia Yu, Danil Prokhorov, Xue Mei, Haibin Ling
2019 arXiv   pre-print
Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named Feature Pyramid and Hierarchical Boosting Network (FPHBN), for pavement crack detection.  ...  The proposed network integrates semantic information to low-level features for crack detection in a feature pyramid way.  ...  CONCLUSION In this work, a feature pyramid and hierarchical boosting network (FPHBN) is proposed for pavement crack detection.  ... 
arXiv:1901.06340v2 fatcat:3q6l3couhvhyzggqaemdlyur6y

Table of contents

2020 IEEE transactions on intelligent transportation systems (Print)  
Miclea and S. Pyramid and Hierarchical Boosting Network for Pavement Crack Detection ...................................... .............................................................. F. Yang, L.  ...  Ke 1415 Deep Learning for Large-Scale Traffic-Sign Detection and Recognition .................... D. Tabernik and D.  ... 
doi:10.1109/tits.2020.2981110 fatcat:kqoxcalcp5gorhifnqgiawxggq

Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images [article]

Kailiang Lu
2021 arXiv   pre-print
To the best of our knowledge, this paper for the first time comprehensively summarizes the pavement crack public data sets, and the performance and effectiveness of surface crack detection and identification  ...  Compared to NDT and health monitoring method for cracks in engineering structures, surface crack detection or identification based on visible light images is non-contact, with the advantages of fast speed  ...  Other algorithms include Feature Pyramid and Hierarchical Boosting Network (FPHBN) [19] , which combines feature pyramid and hierarchical boosting.  ... 
arXiv:2012.14704v2 fatcat:r5tgw2kd6fbubi5ms26kwrfade

RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation [article]

Guijie Zhu, Zhun Fan, Jiacheng Liu, Duan Yuan, Peili Ma, Meihua Wang, Weihua Sheng, Kelvin C. P. Wang
2022 arXiv   pre-print
In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy.  ...  An image data set containing 789 pavement crack images collected by a self-designed mobile robot is constructed and used for training and evaluating the proposed model.  ...  [7] proposed a network named FPHBN that utilizes feature pyramid and hierarchical boosting network to incorporate context information from top to bottom.  ... 
arXiv:2207.14166v1 fatcat:nvqp4667abdv3bm3xhxz6udq4q

Improving the Efficiency of Encoder-Decoder Architecture for Pixel-level Crack Detection

Hanshen Chen, Huiping Lin, Minghai Yao
2019 IEEE Access  
In this study, we propose a simple and effective method to boost the algorithmic efficiency based on encoder-decoder architecture for crack detection.  ...  Thus, a reliable and efficient pixel-level method of crack detection is necessary for real-time measurement of the crack.  ...  In particular, the feature pyramid module [21] is used as a multi-scale feature extractor to capture rich contextual information at different resolutions, and the hierarchical convolutional neural network  ... 
doi:10.1109/access.2019.2961375 fatcat:gqgoijk565b67k2gtlh4akzv7e

NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms [article]

Zhening Huang, Weiwei Chen, Abir Al-Tabbaa, Ioannis Brilakis
2022 arXiv   pre-print
It also found that detecting cracks from concrete pavement images still has huge room for improvement. Dataset for concrete pavement images is also missing in the literature.  ...  Crack detection plays a key role in automated pavement inspection.  ...  Yang et al. (2020) proposed a novel network architecture, named Feature Pyramid and Hierarchical Boosting Network (FPHBN), for pavement crack detection.  ... 
arXiv:2205.01198v1 fatcat:lp4lqqpklrby7jltyhpamcru24

Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection

Qiuchen Zhu, Tran Hiep Dinh, Manh Duong Phung, Quang Phuc Ha
2021 IEEE Access  
In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing.  ...  To this end, this paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for image  ...  network for crack extraction using the typical architecture of SegNet with symmetrical concatenation in the side branch. • FPHBN [22] : The feature pyramid and hierarchical boosting network (FPHBN) is  ... 
doi:10.1109/access.2021.3073921 fatcat:2uzmd6bsg5gm5hyfd3um3em3em

Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network

Stephen L. H. Lau, Edwin K. P. Chong, Xu Yang, Xin Wang
2020 IEEE Access  
INDEX TERMS Convolutional neural network, deep learning, fully convolutional network, pavement crack segmentation, U-Net.  ...  Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images.  ...  These methods are not end-to-end networks and still require feature extraction. Yang et al. [17] proposed a novel architecture that involves feature pyramid and hierarchical boosting modules.  ... 
doi:10.1109/access.2020.3003638 fatcat:lcbto2rnwvhi7kao4zknph7jp4

Automated Pavement Crack Segmentation Using Fully Convolutional U-Net with a Pretrained ResNet-34 Encoder [article]

Stephen L. H. Lau, Xin Wang, Xu Yang, Edwin K. P. Chong
2020 arXiv   pre-print
Automated pavement crack segmentation is a challenging task because of inherent irregular patterns and lighting conditions, in addition to the presence of noise in images.  ...  In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images.  ...  These methods are not end-to-end networks and still require feature extraction. Yang et al. [28] proposed a novel architecture that involves feature pyramid and hierarchical boosting modules.  ... 
arXiv:2001.01912v3 fatcat:cgsmdhiktnesxkopelvn2nbm44

A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images [article]

Liming Jiang, Yuanchang Xie, Tianzhu Ren
2020 arXiv   pre-print
This study introduces a deep neural network method called U-Net for pavement crack segmentation based on drone-captured images to reduce the cost and time needed for airport runway inspection.  ...  In this study, runway pavement images are collected using drone at various heights from the Fitchburg Municipal Airport (FMA) in Massachusetts to evaluate their quality and applicability for crack segmentation  ...  ACKNOWLEDGMENT The authors would like to thank Massachusetts Department of Transportation Aeronautics Division for their financial support and for providing important guidance during the course of this  ... 
arXiv:2001.03257v1 fatcat:bprphricn5cbbcposupzico5m4

TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained Pavement Disease Segmentation [article]

Yujia Zhang, Qianzhong Li, Xiaoguang Zhao, Min Tan
2020 arXiv   pre-print
Existing methods mainly address the tasks of crack detection and segmentation that are only tailored for long-thin crack disease.  ...  Regular pavement inspection plays a significant role in road maintenance for safety assurance.  ...  Another encoder-decoder architecture built on SegNet [2] for semantic segmentation, is also designed to capture hierarchical convolutional features and perform pixel-wise crack detection [40] .  ... 
arXiv:2011.03703v1 fatcat:stfwqimr25c57gvfz7e4qctzke

CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework

Genji Yuan, Jianbo Li, Xianglong Meng, Yinong Li
2022 IET Intelligent Transport Systems  
Automatic crack detection is challenging due to the poor continuity of cracks, the different widths of cracks, and the low contrast between cracks and the surrounding pavement.  ...  The elaborately designed model can effectively suppress the propagation of noise and further refine the crack features by aggregating multiscale and multilevel features from low-level to high-level.  ...  [40] captured a more detailed and comprehensive crack structure by fusing high-level features learned in the hierarchical convolution stage for crack representation.  ... 
doi:10.1049/itr2.12173 fatcat:xw7axh5zjvfiney5dyh34m2e24

A Deep Learning-Based Approach for Road Surface Damage Detection

Bakhytzhan Kulambayev, Gulbakhram Beissenova, Nazbek Katayev, Bayan Abduraimova, Lyazzat Zhaidakbayeva, Alua Sarbassova, Oxana Akhmetova, Sapar Issayev, Laura Suleimenova, Syrym Kasenov, Kunsulu Shadinova, Abay Shyrakbayev
2022 Computers Materials & Continua  
It shows the process of collecting and the features of the training samples and the deep neural network (DNN) training process, taking into account the specifics of the problems posed.  ...  According to the metrics, Mask R-CNN gave the high detection and segmentation results showing 0.9214, 0.9876, 0.9571 precision, recall, and F1-score respectively in road damage detection, and Intersection  ...  The flexibility and prospects of deep learning for the tasks of automatic detection of cracks in the pavement are shown in [14, 15] .  ... 
doi:10.32604/cmc.2022.029544 fatcat:krk7azspercvjelv3suz3agbqi

Scanning the Issue

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
to future electricity and transportation networks.  ...  Siamese neural network is developed to model the environments in mapping, and then similarities can be measured to achieve place recognition for global localization.  ...  Ling A feature pyramid and hierarchical boosting network (FPHBN) is proposed for pavement crack detection.  ... 
doi:10.1109/tits.2020.2980087 fatcat:zncxp7cluzgybbw3zjx4nxefti

Pavement crack detection using non‐local theory and iterative sampling

Zixian Wei, Tao SUN, Yuhao Wu, Liqing Zhou, Xiaoli Ruan
2021 IET Image Processing  
In this paper, a new pavement crack detection algorithm to address these issues is proposed.  ...  However, automatic pavement crack detection is a very challenging task due to noisy texture background, intensity inhomogeneity, and topology complexity.  ...  Yang and Zhang [20] were inspired by feature pyramid and hierarchical boosting network (FPHBN), which integrates context information to low-level features for crack detection in a feature pyramid way  ... 
doi:10.1049/ipr2.12393 fatcat:ay5yxlsi5nf3no7yvpgwtsnxai
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