MATLAB based defect detection and classification of printed circuit board

Abstract

A variety of ways has been established to detect defects found on printed circuit boards (PCB). In previous studies, defects are categories into seven groups with a minimum of one defect and up to a maximum of 4 defects in each group. Using Matlab image processing tools this research separates two of the existing groups containing two defects each into four new groups containing one defect each by processing synthetic images of bare through-hole single layer PCBs.

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

“…Based on it, Putera et al . [5] organised the two existing categories with two defects into four new groups. In the research, each group only contained one type of defect.…”

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

“…Wu et al detect defects using the referential method, and finally classifys them into seven categories [3]. Putera et al classify defects into seven categories by using area characteristics [4]. A referential method is proposed to classify defects into five categories by using edge grey gradient of PCB [5].…”

“…Although referential methods mentioned above all have pretty good performance for PCB defect detection, they are usually computational‐cost and time‐consuming. The requirement of image registration is relatively high, such as methods in [3–5]. For researches using neural networks, most of them mainly put emphasis on the defect classification.…”

Improved bare PCB defect detection approach based on deep feature learning

“…Finally, 13 defects were classified into seven categories. They further improved the algorithm by increasing categories from 7 to 11, and the production cost was reduced in the meantime [10]. Ray et al [11] adopted the Hybrid approach which could not only detect PCB defects but also classify and locate the defects.…”

“…As the literature reviewed above show, although researches of PCB defect detection have made great progress in recent years, most of the researchers remain to employ the image processing techniques to detect PCB defects [8–10]. Also, many researchers adopt a template matching algorithm to the standard image and defected one [7–9].…”