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Title: Soldering defect detection in automatic optical inspection
Authors: Dai, Wenting
Abdul Mujeeb
Erdt, Marius
Sourin, Alexei
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Dai, W., Abdul Mujeeb, Erdt, M., & Sourin, A. (2019). Soldering defect detection in automatic optical inspection. Advanced Engineering Informatics, 43, 101004-. doi:10.1016/j.aei.2019.101004
Project: SMA-RP4
Journal: Advanced Engineering Informatics
Abstract: This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both localization and classifications tasks were considered. For the localization part, in contrast to the existing methods that are highly specified for particular PCBs, we used a generic deep learning method which can be easily ported to different configurations of PCBs and soldering technologies and also gives real-time speed and high accuracy. For the classification part, an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available because it requires domain-specified knowledge. The experiments show that the localization method is fast and accurate. In addition, high accuracy with only minimal user input was achieved in the classification framework on two different datasets. The results also demonstrated that our method outperforms three other active learning benchmarks.
ISSN: 1474-0346
DOI: 10.1016/j.aei.2019.101004
Schools: School of Computer Science and Engineering 
School of Electrical and Electronic Engineering 
Organisations: Fraunhofer Research Center
Rights: © 2019 Elsevier. All rights reserved. This paper was published in Advanced Engineering Informatics and is made available with permission of Elsevier.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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