Deep learning based visual pollutant detection system

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dc.contributor.advisorDr. Mahdy Rahman Chowdhury
dc.contributor.authorIshaq Ali
dc.contributor.authorMd. Sabbir Hossain
dc.contributor.id1530869042
dc.contributor.id1610661042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-12
dc.date.accessioned2024-05-12T06:03:26Z
dc.date.available2024-05-12T06:03:26Z
dc.date.issued2020
dc.description.abstractMost of us are familiar with mainly four types of pollutions. They are 1) air pollution 2) water pollution 3) sound pollution and 4) soil pollution. But a very few of us know about ‘visual pollution’ since it is relatively a new concept. But it is gaining people’s attention slowly. Determining which object should be considered as a visual pollutant is quite a challenge because an object which is visually disturbing to one person may not be visually disturbing to another person. In this study, there are four classes 1) billboards and signage 2) telephone and communication wires 3) network towers and 4) street litter. A deep learning model has been used to detect visual pollutants in an image that was trained and tested on images that we collected using the Google search engine. Our study has a lot of applications in real life like image and video analysis, visual pollutant management, visual pollution index generation, server deployment, and many more. Our study suggests that higher levels of accuracy can be achieved by increasing the size of the dataset.
dc.description.degreeUndergraduate
dc.identifier.cd600000067
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/659
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleDeep learning based visual pollutant detection system
dc.typeProject
oaire.citation.endPage50
oaire.citation.startPage1
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