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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title: Pothole Detection Using Deep Learning And Image Processing
Authors Name: Prashant Solanki , Sushila Palwe
Unique Id: IJSDR2212179
Published In: Volume 7 Issue 12, December-2022
Abstract: Pothole location is one of the main errands for street support. PC vision approaches are by and large in light of either street picture investigation or street surface displaying. In any case, these two classifications are constantly utilized freely. Besides, the pothole recognition precision is still a long way from good. Along these lines, for current framework, we provide a powerful pothole discovery calculation which is exact and computably productive. A thick difference map is primarily changed to more readily recognize harmed and whole street regions. To accomplish more noteworthy difference change effectiveness, brilliant segment search and dynamic writing computer programs are used to assess the change boundaries. This strategy is then used to separate potential flawless street regions from the changed difference map naturally recognize pot opening on street utilizing YOLOv3 engineering with darknet system and profound learning. This framework likewise investigates the chance of custom preparation of YOLOv3 based recognition models utilizing pot opening pictures Knowledge dataset.
Keywords: Yolov3 Algorithm, Roads, Cameras
Cite Article: "Pothole Detection Using Deep Learning And Image Processing", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 12, page no.1114 - 1120, December-2022, Available :http://www.ijsdr.org/papers/IJSDR2212179.pdf
Downloads: 000201575
Publication Details: Published Paper ID: IJSDR2212179
Registration ID:203270
Published In: Volume 7 Issue 12, December-2022
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.32517
Page No: 1114 - 1120
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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