BoardVision: Real-Time Motherboard Defect Detection using YOLOv7 and Faster R-CNN

Simple item page
dc.contributor.authorHill, Brandon
dc.contributor.authorSolaiman, KMA
dc.date.accessioned2025-06-17T14:44:58Z
dc.date.available2025-06-17T14:44:58Z
dc.date.issued2025-04-29
dc.descriptionUMBC CSEE Research Day 2025
dc.description.abstractThis poster introduces BoardVision, a real-time system for detecting motherboard defects using YOLOv7 and Faster R-CNN. Designed for reproducibility and educational use, the tool includes a GUI and supports video, image, and live webcam input. Evaluations show promising accuracy across defect classes with interactive performance.
dc.description.sponsorshipThis project was developed as part of CMSC 478 Introduction to Machine Learning under the supervision of Dr KMA Solaiman Brandon Hill led the implementation and unpublished
dc.format.extent1 page
dc.genreposters
dc.identifierdoi:10.13016/m2ipva-3rks
dc.identifier.citationBrandon Hill and Solaiman KMA, “BoardVision: Real-Time Motherboard Defect Detection Using YOLOv7 and Faster R-CNN,” April 29, 2025.
dc.identifier.urihttp://hdl.handle.net/11603/38812
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectSystem Architecture
dc.subjectYOLOv7
dc.subjectDefect Detection
dc.subjectBoardVision
dc.subjectFaster R-CNN
dc.titleBoardVision: Real-Time Motherboard Defect Detection using YOLOv7 and Faster R-CNN
dc.typeText

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
MotherboardDefectDetection3444.pdf
Size:
1.8 MB
Format:
Adobe Portable Document Format
Download

Collections

UMBC Student Collection
UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection