Journal ArticleDOI
Pavement distress detection and classification based on YOLO network
TLDR
The proposed YOLO-based approach is able to detect PD with high accuracy, which requires no manual feature extraction and calculation during detecting, and significantly outperforms with appropriate illumination.Abstract:
The detection and classification of pavement distress (PD) play a critical role in pavement maintenance and rehabilitation. Research on PD automation detection and measurement has been actively con...read more
Citations
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Journal ArticleDOI
Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm
TL;DR: This study proposes an effective method to automatically perform the recognition and location of concealed cracks based on 3-D ground penetrating radar (GPR) and deep learning models and results reveal that this proposed method is feasible for the detection of hidden cracks.
Journal ArticleDOI
Simulation of hybrid nanofluid flow within a microchannel heat sink considering porous media analyzing CPU stability
Jinyuan Wang,Yi-Peng Xu,Raed Qahiti,M. Jafaryar,Mashhour A. Alazwari,Nidal H. Abu-Hamdeh,Alibek Issakhov,Alibek Issakhov,Mahmoud M. Selim,Mahmoud M. Selim +9 more
TL;DR: In this paper, two models of porous media with the same volume fraction were designed aiming to evaluate the effects of nanomaterial and metallic foam to the conventional solid heat sink on thermal performance, at the constant Reynolds number, the heat sinks which are consist of metallic foam, have better cooling performance and are able to decrease the surface temperature of heat sink more.
Journal ArticleDOI
Cross-scene pavement distress detection by a novel transfer learning framework
TL;DR: A transfer learning pipeline is proposed to address this problem, which enables a distress detection model to be applied to other untrained scenarios and can reduce the demand for training data by at least 25% when the model is applied in a new scene.
Journal ArticleDOI
Pavement distress detection using convolutional neural networks with images captured via UAV
TL;DR: In this paper, a UAV platform for pavement image collection was assembled, and the flight settings were studied for optimal image quality, and collected images were processed and annotated for model training.
Journal ArticleDOI
Deep learning-based road damage detection and classification for multiple countries
Deeksha Arya,Deeksha Arya,Hiroya Maeda,Sanjay Ghosh,Durga Toshniwal,Alexander Mraz,Alexander Mraz,Takehiro Kashiyama,Yoshihide Sekimoto +8 more
TL;DR: In this article, the authors proposed a large-scale heterogeneous road damage dataset comprising 26,620 images collected from multiple countries (India, Japan, and the Czech Republic) using smartphones.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.