NDLI: Wire segmentation for printed circuit board using deep convolutional neural network and graph cut model

Content Provider	IET Digital Library
Author	Qiao, Kai Zeng, Lei Chen, Jian Hai, Jinjin Yan, Bin
Abstract	Printed circuit board wire segmentation based on computed tomography (CT) image can help subsequently locate and estimate inner faults of circuit in an automatic and non-destructive manner. However, CT imaging is prone to suffer from scattered artefacts, metal artefacts and other interference, destroying compact boundary structures of wires. Wires have the characteristic of dense local distribution, and massive vias, pads, and coppers can appear close to wires, resulting in mazy recognition surroundings. The above-mentioned problems bring great difficulty for high-accuracy recognition and location of wire segmentation. In this study, considering that deep convolutional neural network (DCNN) with powerful feature representation can recognise wires in confused surroundings, and graph cut (GC) model relying on grayscale and local texture information specialises in protecting edge structures of wires, the authors propose an effective framework called DCNN-GC that employs DCNN to obtain global semantic prior to guide the GC model to accomplish satisfactory wire segmentation. The authors qualitative and quantitative results demonstrate outstanding performance, and achieve overwhelming intersection over union compared with traditional and DCNN-based methods.
Starting Page	793
Ending Page	800
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 5, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1208
Journal	IET Image Processing
Publisher Date	2018-01-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Circuit Analysis Computing Combinatorial Mathematics Compact Boundary Structures Computed Tomography Image Computer Vision And Image Processing Technique CT Image DCNN Deep Convolutional Neural Network Dense Local Distribution Edge Structure Protection Electronic Engineering Computing Feature Representation GC Model Global Semantic Prior Graph Cut Model Graph Theory Grayscale Information High-accuracy Recognition Image Recognition Image Representation Image Segmentation Image Texture Inner Fault Estimation Inner Fault Location Local Texture Information Massive Vias Metal Artefacts Neural Computing Technique Neural Nets Pads Printed Circuit Printed Circuit Board Scattered Artefacts Wire Segmentation Wires (Electric) Wires And Cables
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

Convolutional neural network-based multi-label classification of PCB defects

Defect detection of printed circuit board based on lightweight deep convolution network

CNN-based reference comparison method for classifying bare PCB defects

Effect of fusing features from multiple DCNN architectures in image classification

Analogue circuit fault diagnosis based on convolution neural network

Improved bare PCB defect detection approach based on deep feature learning

HRIPCB: a challenging dataset for PCB defects detection and classification

Domain adaptation for ear recognition using deep convolutional neural networks

Wire segmentation for printed circuit board using deep convolutional neural network and graph cut model

Similar Documents

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

Convolutional neural network-based multi-label classification of PCB defects

Defect detection of printed circuit board based on lightweight deep convolution network

CNN-based reference comparison method for classifying bare PCB defects

Effect of fusing features from multiple DCNN architectures in image classification

Analogue circuit fault diagnosis based on convolution neural network

Improved bare PCB defect detection approach based on deep feature learning

HRIPCB: a challenging dataset for PCB defects detection and classification

Domain adaptation for ear recognition using deep convolutional neural networks

Wire segmentation for printed circuit board using deep convolutional neural network and graph cut model