research-article

An Improved PCB Defect Detector Based on Feature Pyramid Networks

Authors:

ChuanFu XuAuthors Info & Claims

CSAI '20: Proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence

Pages 233 - 239

https://doi.org/10.1145/3445815.3445853

Published: 17 March 2021 Publication History

Abstract

Aiming at the problem of false alarm in PCB defect detection in the automatic optical inspection process, many researchers have proposed their methods, but most of them only classify the single defect in single image, and there are multiple defects and multiple categories in single image. In this paper, a real PCB data set consisting of 1540 images generated by AOI is introduced for the detection and classification task. In addition, we propose an improved PCB defect detector based on feature pyramid networks. The detector combines Faster R-CNN and FPN as the infrastructure, and has been adjusted and improved, mainly including the following three innovations: 1) SE module is inserted into the feature extraction backbone network resnet-101, which improves the expression ability of network. 2) An enhanced bottom-up structure is introduced to enhance the whole feature level by using accurate low-level positioning signals. 3) ROI Align is used instead of RoI Pooling to reduce the impact of dislocation on small object defect detection. The experimental results show that, compared with the mainstream object detection network, the proposed method achieves better accuracy, reaching 96.3% mAP, and has better performance for defect detection and classification.

References

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Digital Library

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Cited By

Zhou Z(2025)A Comparative Analysis of Printed Circuit Boards Defect Detection Leveraging Deep Learning ApproachesHighlights in Science, Engineering and Technology10.54097/11xrk596124(102-107)Online publication date: 18-Feb-2025
https://doi.org/10.54097/11xrk596
Kiobya TZhou JMaiseli BKhan M(2024)Attentive context and semantic enhancement mechanism for printed circuit board defect detection with two-stage and multi-stage object detectorsScientific Reports10.1038/s41598-024-69207-814:1Online publication date: 5-Aug-2024
https://doi.org/10.1038/s41598-024-69207-8
Wang CHuang GHuang ZHe W(2023)Conditional TransGAN‐Based Data Augmentation for PCB Electronic Component InspectionComputational Intelligence and Neuroscience10.1155/2023/20242372023:1Online publication date: 10-Jan-2023
https://doi.org/10.1155/2023/2024237
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CSAI '20: Proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence

December 2020

294 pages

ISBN:9781450388436

DOI:10.1145/3445815

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 March 2021

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CSAI 2020

CSAI 2020: 2020 4th International Conference on Computer Science and Artificial Intelligence

December 11 - 13, 2020

Zhuhai, China

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Cited By

Zhou Z(2025)A Comparative Analysis of Printed Circuit Boards Defect Detection Leveraging Deep Learning ApproachesHighlights in Science, Engineering and Technology10.54097/11xrk596124(102-107)Online publication date: 18-Feb-2025
https://doi.org/10.54097/11xrk596
Kiobya TZhou JMaiseli BKhan M(2024)Attentive context and semantic enhancement mechanism for printed circuit board defect detection with two-stage and multi-stage object detectorsScientific Reports10.1038/s41598-024-69207-814:1Online publication date: 5-Aug-2024
https://doi.org/10.1038/s41598-024-69207-8
Wang CHuang GHuang ZHe W(2023)Conditional TransGAN‐Based Data Augmentation for PCB Electronic Component InspectionComputational Intelligence and Neuroscience10.1155/2023/20242372023:1Online publication date: 10-Jan-2023
https://doi.org/10.1155/2023/2024237
Wu HTang M(2023)PCB surface defect detection based on improved YOLOv7-tiny2023 5th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)10.1109/MLBDBI60823.2023.10482094(334-337)Online publication date: 15-Dec-2023
https://doi.org/10.1109/MLBDBI60823.2023.10482094
Chen XWu YHe XMing W(2023)A Comprehensive Review of Deep Learning-Based PCB Defect DetectionIEEE Access10.1109/ACCESS.2023.333956111(139017-139038)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3339561
Noroozi MGhadermazi JShah AZayas-Castro J(2023)Toward Optimal Defect Detection in Assembled Printed Circuit Boards Under Adverse ConditionsIEEE Access10.1109/ACCESS.2023.333014211(127119-127131)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3330142
Wu KTan JLiu C(2022)Cross-Domain Few-Shot Learning Approach for Lithium-Ion Battery Surface Defects Classification Using an Improved Siamese NetworkIEEE Sensors Journal10.1109/JSEN.2022.316133122:12(11847-11856)Online publication date: 15-Jun-2022
https://doi.org/10.1109/JSEN.2022.3161331
Yu ZWu YWei BDing ZLuo F(2022)A lightweight and efficient model for surface tiny defect detectionApplied Intelligence10.1007/s10489-022-03633-x53:6(6344-6353)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03633-x
Mankad VBhanvadia NPatel MGajjar R(2021)PCB Classification using Convolutional Neural Network2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N53548.2021.9725695(986-990)Online publication date: 17-Dec-2021
https://doi.org/10.1109/ICAC3N53548.2021.9725695

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