Defect detection of printed circuit board based on lightweight deep convolution network. Issue 15 (18th February 2021)

• Record Type: Journal Article
• Authors: Shen, Jiaquan; Liu, Ningzhong; Sun, Han
• Is Part Of: IET image processing. Volume 14:Issue 15(2020)
• Page Start: 3932
• Page End: 3940
• Publication Date: 2021-02-18
• Subjects: production engineering computing -- automatic optical inspection -- character recognition -- feature extraction -- printed circuit manufacture -- object detection -- convolutional neural nets; object detection -- PCB component defect detection system -- lightweight PCB type detection model -- LD‐PCB -- real‐time detection -- detection accuracy -- fast character recognition model -- robust character recognition model -- called CR‐PCB -- irregular character recognition -- PCB components -- PCB defect detection system -- industrial PCB production -- lightweight deep convolution network -- printed circuit board components -- complex character information -- traditional detection method -- diversified category detection -- deep convolutional neural network; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0841
• Languages: English
• ISSNs: 1751-9659

Abstract

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.