Destruction Detection Using Proprietary Neural Network

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dc.contributor.advisorDr. Atiqur Rahman (AQU)
dc.contributor.authorAbid Hasan Saheel
dc.contributor.authorFahim Hossain
dc.contributor.authorJannatul Ferdous Sristy
dc.contributor.id1912084642
dc.contributor.id1813326642
dc.contributor.id1931533042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-07-16T04:35:59Z
dc.date.available2025-07-16T04:35:59Z
dc.date.issued2023
dc.description.abstractRecently, there has been a boom in Machine learning and deep learning, and rightfully so. Because they have revolutionized the way automation works in any field. And having specialized object detection can automize and make the previously known undoable tasks possible but also make it relatively easy. And as the name suggests, detecting instances of semantic objects of a specific class (such as people, buildings, or cars) in digital photos and videos is the task of object detection. A branch of computer science linked to computer vision and image processing. And using specialized Object recognition, which is a more intensive object detection system. We used it to spot damage on vehicles through image processing. And now our project can correctly classify if a car is damaged or Not Damaged. We used a sequential neural network to train our model. Which went through 120 epochs and achieved an accuracy of 0.98. It also obtained precision, recall, and F1 scores of 0.83, 0.71, and 0.79, respectively. When a prediction value is obtained. It is based on a threshold of 0.5, the image is classified as "Car Damage" or "Car Not Damage." With this result, we can implement the model into any detection system, and it will detect a car and its damage value precisely, even with newer images. Keywords: Object Detection, Machine learning, Deep Learning, Neural Network, Damage detection, Vehicle damage.
dc.description.degreeUndergraduate
dc.identifier.cd600000184
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1274
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU LIbrary
dc.titleDestruction Detection Using Proprietary Neural Network
oaire.citation.endPage50
oaire.citation.startPage1
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