City Traffic Object Detection from Aerial View by Drone Using Computer Vision

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dc.contributor.advisorDr. Rajesh Palit
dc.contributor.authorKazi Shehjad Islam
dc.contributor.authorMd. Rafsan Khan
dc.contributor.authorMd. Akib Dewan
dc.contributor.authorAbdullah Al Mamoon
dc.contributor.id1722198042
dc.contributor.id1812403042
dc.contributor.id1811100042
dc.contributor.id1621845642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-08-13T06:26:55Z
dc.date.available2025-08-13T06:26:55Z
dc.date.issued2022
dc.description.abstractDeep learning algorithms have created a new wave of technologies that are affecting the world as we know it .From movie sites, food sites to international security, deep learning has been used in every case. As we said before, its use is very versatile. We can also use it in traffic object detection which we have tried to do in our project. Using this detection method we can count the number of vehicles on any road using its images or video footage and find the density of it. We used YOLOv5 and Faster R-CNN algorithms for doing this and we have successfully identified the vehicles on the roads. Object detection on drone podium abide a challenging assignment due to various factors such as plot of lookout, alter, barrier, balance. In dainty, large-scale drone-based A dataset boast 8,599 images (6,471 for training, 548 for validation, and 1,580) extravagantly glossed including object bounds for arduous Boxes, object categories, occlusion, truncation caliber, etc. One such algorithm is YOLOv5, developed in 2020. In this delving, every existing YOLOv5 architecture for small target detection is improved by modifying the YOLOv5 configuration. Better performance for small objects by adding a new feature fusion layer in the feature pyramid part of YOLOv5.
dc.description.degreeUndergraduate
dc.identifier.cd600000904
dc.identifier.print-thesisTo be Assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1368
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleCity Traffic Object Detection from Aerial View by Drone Using Computer Vision
oaire.citation.endPage55
oaire.citation.startPage1
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