Toward Optimal Defect Detection in Assembled Printed Circuit Boards Under Adverse Conditions
Author:
Affiliation:
1. Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10309108.pdf?arnumber=10309108
Reference46 articles.
1. High-resolution optical inspection system for fast detection and classification of surface defects
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4. YOLOv4: Optimal speed and accuracy of object detection;bochkovskiy;arXiv 2004 10934,2020
5. An Improved PCB Defect Detector Based on Feature Pyramid Networks
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2. Detection and Classification of Defects on Printed Circuit Board Assembly through Deep Learning;2024 9th International Conference on Smart and Sustainable Technologies (SpliTech);2024-06-25
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