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

Content Provider	IET Digital Library
Author	Shen, Jiaquan Liu, Ningzhong Sun, Han
Abstract	With the rapid development of the electronic industry, the defect detection of printed circuit board (PCB) components is becoming more and more important. The types of PCB components are diverse and accompanied by complex character information, which is difficult to identify. The traditional detection method is inefficient, and it is unable to effectively perform the diversified category detection of PCB components and character recognition in complex scenes. The deep convolutional neural network has obvious advantages in object detection and character recognition, which can be used to implement a PCB component defect detection system. In this study, the authors have established a lightweight PCB type detection model called LD-PCB, which can perform real-time detection while improving detection accuracy. In addition, in the character detection of PCB, they have established a fast and robust character recognition model, called CR-PCB. This model can effectively improve the accuracy of irregular character recognition. Finally, they established and published a dataset of PCB components, and combined with LD-PCB and CR-PCB to realise the PCB defect detection system. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production. The results show that the method proposed in this study can effectively detect defects on PCB components.
Starting Page	3932
Ending Page	3940
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0841
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Optical Inspection Called CR-PCB Character Recognition Complex Character Information Computer Vision And Image Processing Technique Convolutional Neural Nets Deep Convolutional Neural Network Detection Accuracy Diversified Category Detection Electronic Engineering Computing Fast Character Recognition Model Feature Extraction Image Recognition Industrial Application of IT Industrial PCB Production Irregular Character Recognition LD-PCB Lightweight Deep Convolution Network Lightweight PCB Type Detection Model Object Detection PCB Component PCB Component Defect Detection System PCB Defect Detection System Printed Circuit Board Component Printed Circuit Manufacture Production Engineering Computing Real-time Detection Robust Character Recognition Model Traditional Detection Method
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

CNN-based reference comparison method for classifying bare PCB defects

Improved bare PCB defect detection approach based on deep feature learning

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

TDD-net: a tiny defect detection network for printed circuit boards

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

Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks

HRIPCB: a challenging dataset for PCB defects detection and classification

Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder

Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once

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

Similar Documents

CNN-based reference comparison method for classifying bare PCB defects

Improved bare PCB defect detection approach based on deep feature learning

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

TDD-net: a tiny defect detection network for printed circuit boards

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

Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks

HRIPCB: a challenging dataset for PCB defects detection and classification

Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder

Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once

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