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dc.contributor.advisorUddin, Jia
dc.contributor.advisorAshraf, Faisal Bin
dc.contributor.authorBasher, Rafsan
dc.contributor.authorAyon, Asif Raihan
dc.contributor.authorGharamy, Avijit
dc.contributor.authorZayed, Abdullah Al
dc.contributor.authorZaman, Md Samin Yeasar Ibna
dc.date.accessioned2022-09-27T07:31:07Z
dc.date.available2022-09-27T07:31:07Z
dc.date.copyright2022
dc.date.issued2022-01
dc.identifier.otherID 17301042
dc.identifier.otherID 17301170
dc.identifier.otherID 19101519
dc.identifier.otherID 17301126
dc.identifier.otherID 17101533
dc.identifier.urihttp://hdl.handle.net/10361/17354
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 33-36).
dc.description.abstractBangladesh is a fast-developing country, and the number of roads increasing with it is immense. With the ever-growing amount of road comes the age-old problem of a pothole. This paper represents a model of deep learning-based, real-time pothole detection for finding and avoiding road accidents. Any types of image processingbased detection, in this case, pothole detection, are done through various steps. For example, collecting data sets is one of the most crucial steps to create any recognition system. Labeling an image means pinpointing the subject which we will be trying to find. Training the algorithm through those images to detect the subjects is critical in detecting potholes. In this research paper, to detect potholes from real-time videos, firstly, we collected data sets containing more than 600 images of potholes. After that, we labeled those images through labeling software. Then in chapter-1 we used those images to train the model (MobileNet, Inception-v3) which was detecting potholes from still photos given to it. Next, we used YOLOv5 to detect potholes from real-time feeds. In this proposed system, by using the real-time feed, potholes will be detected. Moreover, this will help the masses to detect potholes on roads to avoid accidents, and it will also help people related to the road works to find the potholes for further road maintenance.en_US
dc.description.statementofresponsibilityRafsan Basher
dc.description.statementofresponsibilityAsif Raihan Ayon
dc.description.statementofresponsibilityAvijit Gharamy
dc.description.statementofresponsibilityAbdullah Al Zayed
dc.description.statementofresponsibilityMd Samin Yeasar Ibna Zaman
dc.format.extent36 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectRoaden_US
dc.subjectPotholeen_US
dc.subjectDeep learningen_US
dc.subjectImage processingen_US
dc.subjectReal-timeen_US
dc.subjectMobileNeten_US
dc.subjectInception- v3en_US
dc.subjectYOLOv5en_US
dc.subject.lcshDeep learning (Machine learning)
dc.subject.lcshImage processing -- Digital techniques.
dc.subject.lcshCognitive learning theory (Deep learning)
dc.titleDeep learning-based real-time pothole detection for avoiding road accidenten_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science and Engineering


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