Journal ArticleDOI
Soldering defect detection in automatic optical inspection
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TLDR
An integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs) is proposed and an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available.About:
This article is published in Advanced Engineering Informatics.The article was published on 2020-01-01. It has received 70 citations till now. The article focuses on the topics: Semi-supervised learning & Active learning (machine learning).read more
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Journal ArticleDOI
Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing
Jacqueline Schmitt,Jochen Bönig,Thorbjörn Borggräfe,Gunter Beitinger,Jochen Deuse,Jochen Deuse +5 more
TL;DR: Results show that by employing a new integrated solution of predictive model-based quality inspection in industrial manufacturing by utilizing Machine Learning techniques and Edge Cloud Computing technology, inspection volumes can be reduced significantly and thus economic advantages can be generated.
Journal ArticleDOI
Automatic defect detection of metro tunnel surfaces using a vision-based inspection system
TL;DR: This work designs an automatic Metro Tunnel Surface Inspection System (MTSIS) for the efficient and accurate defect detection, and proposes a multi-layer feature fusion network, based on the Faster Region-based Convolutional Neural Network (Faster RCNN).
Journal ArticleDOI
A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry
TL;DR: The defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained and inspection algorithms used for detecting the defects in the electronic components are discussed.
Journal ArticleDOI
Surface Defect Detection Methods for Industrial Products: A Review
TL;DR: The commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development ofindustrial surface defect detection technology.
Journal ArticleDOI
Autoencoder-based anomaly detection for surface defect inspection
Du-Ming Tsai,Po-Hao Jen +1 more
TL;DR: The experimental results reveal that the proposed CAE with regularizations significantly outperforms the conventional CAE for defect detection applications in the industry.
References
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Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: SSD as mentioned in this paper discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, and combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
Journal ArticleDOI
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
TL;DR: A simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent.
Journal ArticleDOI
A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
Kechen Song,Yunhui Yan +1 more
TL;DR: Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes, and even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy.
Proceedings ArticleDOI
Deep convolutional neural networks for detection of rail surface defects
TL;DR: This paper proposes a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects, and compares the results of different network architectures characterized by different sizes and activation functions.
Book ChapterDOI
Representative sampling for text classification using support vector machines
TL;DR: A straightforward active learning heuristic, representative sampling, is described, which explores the clustering structure of 'uncertain' documents and identifies the representative samples to query the user opinions, for the purpose of speeding up the convergence of Support Vector Machine (SVM) classifiers.