Deep learning for defect detection

With the advancement in technology and growing interest in Artificial Intelligence (AI) related works, there has been increasing reliance on AI to improve the quality and convenience of many monotonous jobs. In the manufacturing and production industry, there is a need to improve operational efficie...

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Bibliographic Details
Main Author: Ho, Juliet Li Chew
Other Authors: Qian Kemao
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175998
Description
Summary:With the advancement in technology and growing interest in Artificial Intelligence (AI) related works, there has been increasing reliance on AI to improve the quality and convenience of many monotonous jobs. In the manufacturing and production industry, there is a need to improve operational efficiency and product quality for consumers. Proper packaging is essential to protect products from damage and assure the quality of products that are delivered to customers. Deep learning is often used for defect detection through image analysis and pattern recognition. Deep learning models such as convolutional neural networks (CNN) are used to learn features from data such as images to detect and classify new images.