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PotholeEye -- How Can We Effectively Maintain the Pavement Distress? (demo)

Authors:

Juyoung Park,

Kiyoung Im,

Jung Hee LeeAuthors Info & Claims

MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

Pages 679 - 680

https://doi.org/10.1145/3307334.3328579

Published: 12 June 2019 Publication History

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Abstract

We propose a mobile system, called PotholeEye, for automatically monitoring the surface of a roadway and providing real-time analysis of images, with the specific goal of detecting the pavement distress. PotholeEye pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. We have tested PotholeEye on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on, and have shown how we can effectively maintain the pavement distress.

References

[1]

Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, and Tsuhan Chen. 2018. Recent Advances in Convolutional Neural Networks. Pattern Recognition 77 (May 2018), 354--377.

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[2]

Juyoung Park, Heuisoon Lee, Kyungtae Kang, and Byung-Hoe Kim. 2011. Pothole detection in asphalt pavement images. Advanced Engineering Informatics 25, 3 (Aug. 2011), 507--515.

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Google Scholar

[3]

Juyoung Park, Heuisoon Lee, Kyungtae Kang, and Byung-Hoe Kim. 2018. Realtime pavement detection based on video analysis and notification service. KIISE Transactions on Computing Practices 24, 2 (Feb. 2018), 59--66.

Crossref

Google Scholar

[4]

David Martin Powers. 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies 2, 1 (Dec. 2011), 37--63.

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Cited By

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Gorintla SKumar BChanadana BSai NKumar G(2022)Deep-Learning-Based Intelligent PotholeEye+ Detection Pavement Distress Detection System2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC53929.2022.9792696(1864-1869)Online publication date: 9-May-2022
https://doi.org/10.1109/ICAAIC53929.2022.9792696
Lee SKim SMoon S(2022)Development of a Car-Free Street Mapping Model Using an Integrated System with Unmanned Aerial Vehicles, Aerial Mapping Cameras, and a Deep Learning AlgorithmJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000101336:3Online publication date: May-2022
https://doi.org/10.1061/(ASCE)CP.1943-5487.0001013

Index Terms

PotholeEye -- How Can We Effectively Maintain the Pavement Distress? (demo)
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Information & Contributors

Information

Published In

MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

June 2019

736 pages

ISBN:9781450366618

DOI:10.1145/3307334

General Chairs:
Junehwa Song
KAIST, South Korea
,
Minkyong Kim
Samsung Electronics
,
Program Chairs:
Nicholas D. Lane
University of Oxford & Samsung AI
,
Rajesh K. Balan
Singapore Management University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2019

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Demonstration

Conference

MobiSys '19

Sponsor:

SIGMOBILE

MobiSys '19: The 17th Annual International Conference on Mobile Systems, Applications, and Services

June 17 - 21, 2019

Seoul, Republic of Korea

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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

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Gorintla SKumar BChanadana BSai NKumar G(2022)Deep-Learning-Based Intelligent PotholeEye+ Detection Pavement Distress Detection System2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC53929.2022.9792696(1864-1869)Online publication date: 9-May-2022
https://doi.org/10.1109/ICAAIC53929.2022.9792696
Lee SKim SMoon S(2022)Development of a Car-Free Street Mapping Model Using an Integrated System with Unmanned Aerial Vehicles, Aerial Mapping Cameras, and a Deep Learning AlgorithmJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000101336:3Online publication date: May-2022
https://doi.org/10.1061/(ASCE)CP.1943-5487.0001013

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Recommendations

A comparison of multi-resolution methods for detection and isolation of pavement distress

Visual analysis of asphalt pavement for detection and localization of potholes

Deep learning-driven pavement crack analysis: Autoencoder-enhanced crack feature extraction and structure classification