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Cite this DOI

10.46243/jst.2020.v5.i3.pp101-110 · Detection of Pothole by Image Processing Using UAV

APA (7th edition)

Saurabh Pehere, Prajwal Sanganwar, Shashikant Pawar, & Prof. Ashwini Shinde (2020). Detection of Pothole by Image Processing Using UAV. *Journal of Science & Technology*, *5*(3). https://doi.org/10.46243/jst.2020.v5.i3.pp101-110

⬇ text Italics are shown as *asterisks* in plain text — the journal or book title and the volume.

BibTeX

@article{saurabhpehere2020detection,
  author    = {Saurabh Pehere and Prajwal Sanganwar and Shashikant Pawar and Prof. Ashwini Shinde},
  title     = {{Detection of Pothole by Image Processing Using UAV}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {may},
  volume    = {5},
  number    = {3},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i3.pp101-110},
  url       = {https://doi.org/10.46243/jst.2020.v5.i3.pp101-110},
  language  = {en},
  abstract  = {Potholes on road can generate costly damage to flat tire or can cause wheel damage of motor cycle. Especially in India vehicle collision and major accidents happens due to immense depth of pothole on road. Thus, detecting and repairing of potholes on road is major challenge for in ITS (Intelligent Transportation System) service and road management system. In order to enhance the efficiency of pavement inspection,currently some new sorts of remote sensing methods without any physical damage to the pavements is possible. In our study the pavement images captured by the unmanned aerial vehicle are collected and distinguish the pavements with the pothole from the normal pavements by performing the Morphological Operations on the images. UAV is new tool under the Remote sensing technique for the pavement inspection with more efficiency as it is usable in hard to reach places also where road maintenance is difficult. Through large transport surveillance, we built a method that can detect pothole using various algorithms of image processing for time optimization of surveillance. A model of potholes is constructed using the image library, which is used in an algorithmic approach that combines a live road footage and simultaneously detection of potholes using simple image processing techniques such as a medium filter and edge detection. Using this approach, it was possible to detect potholes with a precision of 80\% and recall of 74.4.\%. Using UAV, we can reach in remote places and specially to National Highways, detection of pothole and collecting all database become very easy and time saving. With more advanced technologies such as Artificial Intelligence and Machine Learning algorithms can be used for surveillance of road with more precise results with automatic repairing robots or machines}
}

⬇ .bib

RIS (EndNote, Zotero, Mendeley)

TY  - JOUR
TI  - Detection of Pothole by Image Processing Using UAV
AU  - Saurabh Pehere
AU  - Prajwal Sanganwar
AU  - Shashikant Pawar
AU  - Prof. Ashwini Shinde
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/05/13/
VL  - 5
IS  - 3
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Potholes on road can generate costly damage to flat tire or can cause wheel damage of motor cycle. Especially in India vehicle collision and major accidents happens due to immense depth of pothole on road. Thus, detecting and repairing of potholes on road is major challenge for in ITS (Intelligent Transportation System) service and road management system. In order to enhance the efficiency of pavement inspection,currently some new sorts of remote sensing methods without any physical damage to the pavements is possible. In our study the pavement images captured by the unmanned aerial vehicle are collected and distinguish the pavements with the pothole from the normal pavements by performing the Morphological Operations on the images. UAV is new tool under the Remote sensing technique for the pavement inspection with more efficiency as it is usable in hard to reach places also where road maintenance is difficult. Through large transport surveillance, we built a method that can detect pothole using various algorithms of image processing for time optimization of surveillance. A model of potholes is constructed using the image library, which is used in an algorithmic approach that combines a live road footage and simultaneously detection of potholes using simple image processing techniques such as a medium filter and edge detection. Using this approach, it was possible to detect potholes with a precision of 80% and recall of 74.4.%. Using UAV, we can reach in remote places and specially to National Highways, detection of pothole and collecting all database become very easy and time saving. With more advanced technologies such as Artificial Intelligence and Machine Learning algorithms can be used for surveillance of road with more precise results with automatic repairing robots or machines
DO  - 10.46243/jst.2020.v5.i3.pp101-110
UR  - https://doi.org/10.46243/jst.2020.v5.i3.pp101-110
ER  -

⬇ .ris

CSL-JSON

{
    "type": "article-journal",
    "id": "10.46243/jst.2020.v5.i3.pp101-110",
    "DOI": "10.46243/jst.2020.v5.i3.pp101-110",
    "URL": "https://doi.org/10.46243/jst.2020.v5.i3.pp101-110",
    "title": "Detection of Pothole by Image Processing Using UAV",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Saurabh Pehere"
        },
        {
            "family": "Prajwal Sanganwar"
        },
        {
            "family": "Shashikant Pawar"
        },
        {
            "family": "Prof. Ashwini Shinde"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2020,
                5,
                13
            ]
        ]
    },
    "volume": "5",
    "issue": "3",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Potholes on road can generate costly damage to flat tire or can cause wheel damage of motor cycle. Especially in India vehicle collision and major accidents happens due to immense depth of pothole on road. Thus, detecting and repairing of potholes on road is major challenge for in ITS (Intelligent Transportation System) service and road management system. In order to enhance the efficiency of pavement inspection,currently some new sorts of remote sensing methods without any physical damage to the pavements is possible. In our study the pavement images captured by the unmanned aerial vehicle are collected and distinguish the pavements with the pothole from the normal pavements by performing the Morphological Operations on the images. UAV is new tool under the Remote sensing technique for the pavement inspection with more efficiency as it is usable in hard to reach places also where road maintenance is difficult. Through large transport surveillance, we built a method that can detect pothole using various algorithms of image processing for time optimization of surveillance. A model of potholes is constructed using the image library, which is used in an algorithmic approach that combines a live road footage and simultaneously detection of potholes using simple image processing techniques such as a medium filter and edge detection. Using this approach, it was possible to detect potholes with a precision of 80% and recall of 74.4.%. Using UAV, we can reach in remote places and specially to National Highways, detection of pothole and collecting all database become very easy and time saving. With more advanced technologies such as Artificial Intelligence and Machine Learning algorithms can be used for surveillance of road with more precise results with automatic repairing robots or machines",
    "ISSN": "2456-5660"
}

⬇ .json What citeproc and reference managers read; the DOI system hands it out for Accept: application/vnd.citationstyles.csl+json, and so does this registry's resolver.

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i3.pp101-110 gives all four in one JSON answer.

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