Circuit board defect detection based on image processing

Ren, Shuyan; Liu, Lu; Zhao, Li; Duan, Hailong

doi:10.1109/cisp.2015.7408005

Cited by 15 publications

(6 citation statements)

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“…Ren et al [9] proposed a referential method which utilised the edge grey gradient of the PCB image in order to classify defects into five defined classes. In [10], all 14 types of defects were detected and classified in all possible classes using the referential inspection approach.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Jin

Yang

et al. 2018

J. eng.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Jin

Yang

et al. 2018

J. eng.

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“…Ren et al (8) applied a method involving the use of the edge of an image to quickly detect and locate defects on circuit boards and increase the detection speed on printed circuit boards (PCBs). The algorithm accounted for the gray scale characteristics of the edge images.…”

Section: Introductionmentioning

confidence: 99%

Application of Line Scan Technology for Defect Inspection in Plain Dyed Fabric

Chen¹,

Tsai²,

Ding³

et al. 2021

Sensors and Materials

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Fabrics are susceptible to defects caused by various factors during the dyeing and finishing processes. Fabric defects are mostly detected through manual visual inspection. Under the influence of fatigue from long working hours and human error in general, this method results in misjudgment and missed defects. Certain difficulties are encountered in the development of automated optical inspection (AOI) machines because large pieces of fabric must be inspected at high speed. In this study, an AOI system was developed. The image acquisition system consisted of an IDS industrial camera equipped with a sensor for fabric defect inspection and a line scan function, as well as a white light-emitting diode (LED) light bar. The parameters were configured and calibrated using uEye Cockpit software. The inspection mechanism of the proposed machine was a roller device that simulated fabric production. Under continuous rotation of the roller, images were captured using the camera and then were combined to form a two-dimensional plane image. Long-term inspection was performed simultaneously. The line scan settings were a pixel clock (PCLK)S rate of 474 MHz and 10 scan lines per single scan when the tangential rotation velocity of the roller simulation was 0.9 m/s. The minimum vertical distortion of the acquired image was 1.658 mm when the total image length was 210 mm. Uneven illumination from the nonlinear light source resulted in the generation of noise in the captured images, which was eliminated through the combination of posterization and closing, with only a slight loss of defect features. Overall, the proposed AOI system effectively detected the defects. The minimum width of black defects that could be detected in the dyed and finished dark fabrics was 0.24 mm.

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“…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]. Ibrahim et al improve PCB detection by incorporating a geometrical image registration.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%