Automated Vision-Based Inspection and Defect Detection for Industrial Applications

Overview

Identifying product defects and reducing manufacturing errors in industrial applications can help reduce labor and manufacturing costs. While traditional techniques for automated optical inspection tend to be brittle, deep learning based techniques are more robust and accurate.

Whether you are new to deep learning or an expert, MATLAB^® can help you detect and localize different types of abnormalities so you can replace traditional inspection processes with accurate, repeatable, and reliable vision inspection. In this webinar we will talk about

• Automating preparation and labeling of training data
• Interoperability with open source deep learning frameworks
• Training deep neural networks for vision applications
• Tuning hyper-parameters to accelerate training time and increase network accuracy
• Generating multi-target code for NVIDIA^®, Intel^®, and ARM^®

About the Presenter

Rishu Gupta is a senior application engineer at MathWorks. He primarily focuses on image processing, computer vision and deep learning applications. Rishu has an experience of over 9 years working on applications related to visual contents. He previously worked as a scientist at LG soft India, research and development unit. He has published and reviewed papers in multiple peer-reviewed conferences and journals. Rishu holds bachelor’s degree in electronics and communication engineering from BIET Jhansi. Master’s in visual contents from Dongseo University, South Korea, working on the application of computer vision. PhD in electrical engineering from University Technology Petronas, Malaysia with focus on Biomedical Image Processing using ultrasound images.