Defects Detection and Classification on Metallic Surfaces
Abstract (SE9)
Inspecting metallic parts is a challenging task and is in demand in various manufacturing quality control applications. Computer Visions (CV), Machine Learning (ML) and Deep Learning (DL) are typically used for automating the inspection process, in particular, for detection, recognition, and classification of metallic surface imperfections. The objectives of this research are bifold: 1) programming a robotic arm to pick and drop objects and capture multiple images of the object using a stationary camera and 2) developing the CV, ML, and DL algorithms for identifying defects such as dents, scratches, glue and paint marks on smooth metal surfaces.
Student Authors: Kseniia Gromova and Youxin Zhuo
Faculty Advisor: Vinayak Elangovan
About the Presenters:
Kseniia Gromova is a senior student majoring in Computer Science at Penn State Abington. Her research interests include Machine Learning, Artificial Intelligence, and Computer Vision.
Youxin Zhuo is a Junior student majoring in Computer Science at Penn State Abington. His research interests include Machine Learning, Android Studio, and Web Development.
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