NDLI: Unknown defect detection for printed circuit board based on multi-scale deep similarity measure method

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Unknown defect detection for printed circuit board based on multi-scale deep similarity measure method

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Unknown defect detection for printed circuit board based on multi-scale deep similarity measure method

Content Provider	IET Digital Library
Author	Ding, Runwei Zhang, Can Zhu, Qisheng Liu, Hong
Abstract	Defect detection with high precision is of great significance for printed circuit board (PCB) fabrication. Due to the lack of priori knowledge of categories and shape features, detection of unknown defects faces greater challenges than that of common defects. Inspired by similarity measurement, this study proposes a multi-layer deep feature fusion method to calculate the similarity between template and defective circuit board. Compared with conventional methods which divide the whole detection into two independent parts of hand-designed features and similarity measurement, the authors end-to-end model is designed to combine these two parts for joint optimisation. First, the Siamese network is utilised as their backbone architecture for feature extraction of pairwise images. And then the spatial pyramid pooling network is incorporated into the feature maps of each convolutional module to fuse the multi-scale feature vectors. Finally, the discriminative feature embedding and similarity metric are obtained by using the contrastive loss during the training process. Experimental results show that the proposed model has better performance in detecting and locating unknown defects in bare PCB images than traditional similarity measurement methods. Moreover, our method is promising for further improvement of defect detection with less training image pairs and more accurate detection results.
Starting Page	388
Ending Page	393
Page Count	6
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1188
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Automatic Optical Inspection Bare PCB Image Computer Vision And Image Processing Technique Convolutional Module Convolutional Neural Nets Defective Circuit Board Discriminative Feature Embedding Electronic Engineering Computing End-to-end Model Feature Extraction Feature Maps Hand-designed Feature Image Fusion Image Recognition Industrial Application of IT Inspection And Quality Control Joint Optimisation Multilayer Deep Feature Fusion Method Multiscale Deep Similarity Measure Method Multiscale Feature Vector Neural Computing Technique Object Detection Optimisation Optimisation Technique Pairwise Image Printed Circuit Board Fabrication Printed Circuit Manufacture Production Engineering Computing Shape Feature Shape Recognition Siamese Network Spatial Pyramid Pooling Network Template Circuit Board Unknown Defect Detection
Content Type	Text
Resource Type	Article

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