Generative AI-Driven PCB Defect Detection and Classification

System

Gayatri M. Phade, Sahil Papal, Saish Aher and Sanket Chaudhari

Department of Electronics and Telecommunication, Sandip Institute and Technology and Research Centre, Nashik, India

Keywords: Image Processing, Image Pre-Processing, Image Acquisition, Fault Detection, PCB Inspection.

Abstract: Traditionally, PCB testing is done manually. It has limitation like it is a time-consuming process, high cost,

human error, low accuracy etc. This manual process leads to high processing time, increased product cost

and quality. To overcome this limitations Generative AI enabled PCB testing System is proposed that will

detect and classify the defects in PCB. The Generative AI Based PCB Tester (GABPT) system uses AI

enabled image processing to make the process faster and more accurate. The GABPT classify defects like

scratch, missing holes, Missing Path, Mouse-bite, Short-circuit, missing Conductor etc. The main objectives

to reduce human error, speed up testing, and lower the testing costs.it will give an approach on machine-based

methods like emphasizing image pre-processing, image acquisition and analysis. The system uses high-

resolution cameras to capture the relative image of the PCB to be tested, an Arm Cortex A72 processes for

analysing the captured images and LCD to monitor the tested result. It identifies and categorizes defects,

providing immediate feedback to help users resolve issues efficiently. This paper improves the efficiency of

PCB testing by using automation. This helps save time and money while making the process more accurate.

As a result, we proposed a better--quality products and reduce human errors, which encourages innovation in

the industry. By using resources more efficiently, we also support more sustainable manufacturing practices.

1 INTRODUCTION

The electronics industry is advancing rapidly,

creating an increasing need for high-quality printed

circuit boards (PCBs). As these boards grow more

complex, effective testing methods become essential

to ensure their reliability and performance.

Traditional manual testing is often slow, susceptible

to human error, and may overlook subtle defects.

(Rao, Abhinav, et al. , 2024)

In this paper a fully automated ai based PCB tester

system is proposed to meet these challenges. The

purpose of the proposed system is to develop an

automated system that uses generative ai and

advanced image processing in order to enhance the

speed and accuracy with which PCBs can be tested.

Our system utilizes machine vision, automatically

recognizing and classifying defects such PCB-

scratches, misalignments, and shorts-automatically. It

thereby not only makes quality control processes

much more efficient but also less vulnerable to the

risk of human error entailed by manual inspections.

Image preprocessing is an integral part of the

proposed system. It deals with the enhancement of

PCB images for quality purposes before analysis.

Various operations on the PCB images were

performed, which include noise reduction, contrast

enhancement, and edge detection. For example, noise

reduction is helpful in removing unwanted artifacts

masking defects. Contrast enhancement brings the

key features to prominence and edge detection

outlines the boundary of components and defects

thereby improving classification accuracy.

Crucial to PCB tester employs is the data

collection method; it is a critical requirement for

training the ai model. We compiled a diverse set of

images of PCBs with different types of defects,

ensuring that every probable issue of the defect is

accounted for. This dataset was acquired from

existing databases as well as the images captured

during the actual manufacturing process itself. We

have taken special care to cover very common as well

as rare defects to make the model robust. This

extensive dataset we have gathered, which

encompasses common and rare defects, assists in the

improvement of the precision of the model as it

detects and classifies the defects. High resolution

cameras coupled with advanced algorithms were used

Phade, G. M., Papal, S., Aher, S. and Chaudhari, S.

Generative AI-Driven PCB Defect Detection and Classiﬁcation System.

DOI: 10.5220/0013588900004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 2, pages 181-188

ISBN: 978-989-758-763-4

181

in our system to make it possible for it to accomplish

the tasks we had in mind.

The TensorFlow library was the driving force of

all machine learning activities we planned; this is

because it allows the model to learn what we want

from a huge collection of PCB images. That way, our

ai could easily identify some defects and continually

perfect its defect identifying capabilities. We utilized

the OPENCV library for image processing tasks. The

OPENCV will assist in bringing useful tools for

image manipulation into our preprocessing efforts.

Filtering and edge detection methods make the

images of better quality before analysis by the ai.

TensorFlow and OPENCV were added to enhance the

capabilities of our system regarding instant feedback

on defect detection. From the hardware perspective,

our generative ai-based PCB tester is built around a

raspberry pi 4b+ with the arm cortex a72 processer as

the processing unit. It is small yet powerful and

capable of processing real-time images, along with ai

computation. This will enable us to form a

harmonious whole by effectively integrating these

components.

To download high-resolution images required to

detect any faults, we have the camera module v2. In

that camera, the resolution is so good; hence it will be

possible for us to download the images of the printed

circuit boards in which even small fault is detectable.

For training our ai model with various faults, images

in high definition are very crucial for the purpose. The

user-friendly touch screen display comes with our

generative ai-based PCB tester, which can be easily

integrated with the raspberry- pi. The interface of this

system is developed to be simple and practical so that

users, the operators, can easily get things done in the

system. One of the impressive features of this display

is that it depicts its results in real time while

inspection is underway.

Real time results during inspections, is one of the

features of the display. The instant feedback of

images after they are captured and analysed is what

operators will see in case defects are detected. This

real-time information allows them to quickly decide

what action needs to be taken next—be it reworking

a PCB or specific quality-related issues. Overall, the

touchscreen display dramatically enhances the

usability of our PCB tester. It simplifies the tasks

facing the operators while managing the inspections,

thereby enhancing quality production in the PCB

manufacturing process. There will be improvements

in the system itself, and future dataset growth will see

newer types of defects that might evolve at the

production stage. Improvement of our algorithms

regarding accuracy levels and processing times is also

something to which we are committed. We also

foresee, in the future, adding new functionalities such

as predictive maintenance features for manufacturers

whereby they can predict problems before it happens.

In this paper, we detail the methodologies behind

generative ai approach through describing the image

preprocessing techniques and detection algorithms

that we used. We also discuss some of the issues we

have faced in development and how our system can

be smoothly integrated into any manufacturing

process. Our aim is to demonstrate that this advanced

testing solution will significantly increase the

efficiency of defect detection even in images of very

low quality, thus promoting the production of more

reliable electronic components.

2 RELATED WORK

In recent years, various methods have emerged for

detecting faults in printed circuit boards (PCBs).

Initially, the process relied on manual inspections

using magnifying tools. Unfortunately, this method

was often insufficient because small defects were

difficult to spot. This challenge led the industry to

explore image processing systems that utilize

technology for more effective fault detection. These

image processing systems work by capturing a

photograph of the PCB being inspected and

comparing it to a reference image of a perfect PCB.

This comparison makes it easier to identify any

discrepancies or faults that might be missed during

manual inspections. A major advantage of this

approach is its speed; it can quickly detect a variety

of defects. (Rao, Abhinav, et al. , 2024), (Raj, and,

Sajeena, 2018).

Recent studies have produced outstanding

advances in PCB flaw identification by using

different methods. A certain study looked at the use

of artificial neural networks (ANN) and resistance

analysis. Researchers were able to distinguish

between crucial characteristics such as traces and

contacts and probable flaws using thinning methods

and clustering approaches. This method relates the

trace current shifts to fault features, it boosting both

visual and electrical investigations, which assists in

properly spotting issues even tiny issues. (Lee, and,

Kim, 2021), (Thomas, Sutar, et al. , 2017), (Cheng

and Liu, 2022)

Research also involves a small scanning parts of

PCB images to detect the PCB defects. This method

is flood-filling, k-means clustering, and statistical

analysis to focus on specific components. By

analysing these smaller sections, it increases

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precision and efficiency across various types of

PCBs. (Melnyk, and, Shpek, 2023), (Malin, et al. ,

2022)

A comparative study looked at different imaging

techniques—visible light, x-ray, and near-infrared

(NIR)—for defect detection. While x-rays are

effective for revealing internal flaws, they come with

health risks due to radiation exposure. The research

emphasizes NIR images as a safer and equally

reliable option, it also suggesting it could effectively

replace x-ray inspections. (Malin, et al. , 2022),

(Yadav, Gupta, et al. , 2021), (Cheng and Liu, 2022)

A paper proposed on a machine learning system

for to detect defects such as missing components and

circuit breaks using the yolo (you only look once)

algorithm. This approach allows for real-time

identification and classification of defects by

processing grayscale images and employing edge

detection, enhancing visibility and speeding up

inspections. (Yadav, Gupta, et al. , 2021), (Purva,

Shubhangi, et al. , 2022).

Additionally, a different study utilized image

processing techniques like median filtering to reduce

noise and the Sobel operator for edge detection. By

applying template matching to compare test images

with a standard reference, this system improves both

the accuracy and efficiency of defect detection. (Cai,

Li, et al. , 2012)

An automated visual inspection system detailed in

another study used a subtraction algorithm to

compare inspected PCBs with a standard reference.

This method assessed the impact of noise on detection

accuracy and categorized various defects, such as

missing holes and short circuits, thereby improving

the reliability of inspections in industrial settings.

(Raj and Sajeena, 2018)

Other research works show that convolutional

neural networks (CNNs) are great at detecting defects

in images of printed circuit board (PCB) as they can

automatically learn key features for the classification

task. To further improve effectiveness, researchers

attempt various applications of data augmentation,

such as rotating and flipping images, during training.

They also adopt transfer learning, which is the fine-

tuning of pre-trained models toward PCB inspection,

especially when labelled data is in short supply. The

other approach is that f anomaly detection, which

identifies defects through comparisons made between

images of PCBs and defect-free images. The methods

include u-net and mask r-CNN, which allow for full

inspection by automatically breaking down images

into distinctive parts. (S. A., et al. , 2025), (Hu and

Wang, 2020), (Cheng and Liu, 2022)

Finally, research on the image subtraction method

approaches the studied identifying faults such over-

etching and under-etching, use of segmentation and

thresholding methods along with neural networks for

efficient classification, which leads to faster and more

accurate results. (Mishra, Das, et al. , 2020)

3 METHODOLOGY

The proposed method for the system involves the

acquisition of two PCB images one is a reference

image that has no defects and another is a test image

of the same design that may contain defects then

image pre-processing, thresholding, defect detection

and identification is carried out. The resultant image

is the defects present in the test PCB that can located.

3.1 Image Acquisition

Figure 1 shows that image acquisition is the

acquisition of images of real-world objects through

devices such as cameras or scanners. The process, in

our case, would be acquiring high-resolution images

of PCBs that would later on be sent for image

processing and analysis. To ensure the success of this

operation, we acquire images using high-resolution

cameras or scanners. We also created a controlled

lighting environment that has less shadows and

reflections so the images are clear. Again, a stable

camera platform is essential for the camera to have

stable image quality. We take the sufficient number

of pictures of the different PCBs so as to prepare the

robust dataset for analysis.

Figure 1: Image acquisition

3.2 Image Preprocessing

Image preprocessing is simply the set of techniques

applied to improve the quality of raw images into

clearer ones ready for analysis.

In proposed system, this improvement of PCBs

images comes right before our search for defects. This

Generative AI-Driven PCB Defect Detection and Classiﬁcation System

183

is important because the quality of these images may

determine exactly how accurate our results will be.

Often, such raw images arrive with various noise-like

shadows or multiple illuminations that might mask

some important details. Applying noise reduction and

contrast enhancement, they can be made clearer and

therefore much more useful for our analysis. Equation

1 is used for image preprocessing.

(f*g)(x,y) =

∑



-k

∑



-kf(x-I,y-j).g(I,j) (1)

f (x, y) is the input image.

g (i, j) is the convolution kernel.

i, j are the indices of the kernel.

the result is the filtered image at

position (x, y).

In image preprocessing to remove the noise from the

image, the following formulae, given by equation 2,

is used

g(x,y) =



∗

∑



-k

∑



-kf(x+I,y+j)

After the preprocessing, the image is converted into

grey scale using equation 3.

Greyscale = 0.29*red+0.58*green+0.11*blue---

3.3 Image Segmentation

Figure 2: Image segmentation

Figure 2 shows two parts the segmentation of an

image where the specific regions can be captured for

discussion. To deal with the defect-detection system

in printed circuit boards, this aspect is mainly

required to separate components and capture traces,

pads, and holes. Thus, we could more accurately

capture defects.

In our GABPT system, we use techniques like

thresholding to separate based on the brightness of

areas and edge detection to draw focus on the

boundaries of the components. This allows zooming

in on areas that are most likely to have defects.

Thresholding distinguishes copper traces from board

surface, edges detect important borders, breaking

down an image into its component parts increases the

accuracy of defect detection, so that every part of the

PCB is dealt with in respect of any short circuits or

misalignments as well as missing components

3.4 Proposed System

The figure 3 shows the block diagram of proposed

system. Arm cortex-a72 is one of the most powerful

processors ever produced with armv8-a architecture,

which carries a 64-bit instruction set with wide

performance in computer operations. It has a quad

core structure that makes it very efficient in its real-

time tasks like inspecting images of PCB for defects.

Energy efficiency is guaranteed since the battery life

is not drained while handling demanding jobs that

makes it ideal for portable devices.

One of the main features of cortex-a72 is the

support for machine learning, so quite apt for

generative ai in PCB testing which quickly and

accurately detects and classifies defects. Integrated

graphics improve the visualization of layouts across

PCBs. Furthermore, its architecture supports

flexibility through multiple adaptation manners:

prototyping or production. Overall, cortex-a72

accelerates PCB quality assessment speed and

accuracy in a line so that it is more efficient for the

manufacturing process and for products altogether.

We rely on a camera to capture images of printed

circuit boards (PCBs). The 11.9 mega pixel camera is

cost effective that delivers the high-quality images

while seamlessly integrating with tools, and for image

analysis purposes too. This method significantly

enhances our ability to inspect PCB layouts

accurately. To keep the raspberry pi 4b running

smoothly, it needs a 5v dc power supply. The power

supply provides 5v dc supply through ac to dc

conversion. To make GABPT system even more user-

friendly, we've added a 7-inch touchscreen display.

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This allows for easy interaction, making it simple to

input data, navigate menus, and receive visual

Figure 3: Block diagram of GABPT

feedback. It's perfect for any projects that require

real-time responses or interactive controls, providing

a more engaging experience for your users.

The figure 4 shows the flowchart of PCB tester

which illustrates the overall objective of the PCB

defect detection system is to make the inspection of

printed circuit boards more efficient. It starts with

expert inspectors who closely scrutinize the boards

for any defects. These inspectors take high-quality

images that act as the basis for further analysis. Once

these images are collected, then comes the

enhancement process. This enhancement process

would involve the following: adjustment of

brightness and contrast, noise removal, and

segmentation of PCBs from their backgrounds. This

stage also extracts critical characteristics indicative of

flaws.

Figure 4: Flow chart of PCB tester

After processing images, this is then sent for

validation check where data collected here needs to

be verified to be complete and accurate. In case some

errors are produced, the team returns to collect more

data that will correct such errors. The validated data

is then found in a centralized database for easy access

and analysis. More advanced tools, such as

OPENCV, are used to further analyse the images.

Such tools may aid in the image segmentation process

and examine each part of the PCB closely to look for

defects. This approach makes it sufficiently extensive

to enable machine learning integration into the

project.

It employs one of the primary ingredients, which

are frameworks such as TensorFlow and keras to

develop and then train its CNN. Once trained on

them, these models become rather effective in defect

detection based upon the features that have been

extracted from images. They can spot defects

accurately in new PCB images. The analysis of the

results obtained with these models will draw out

patterns and trends that can bring about root causes of

defects and a good amount of insight in regard to

improved manufacturing.

For easy information access, a user-friendly

dashboard is developed. The interface allows users to

interact easily with the outcome of inspection results

and produce a customized report about the object

under test. In a general view, the structured workflow

improves image processing and defect detection and

provides critical insight into optimizing

manufacturing practices.

4 TYPES OF DEFECTS UNDER

TESTING

In a defective PCBs, there are some different types of

defects which can be classified into the following

categories

Fig 5 shows the PCB with pin-hole defect, it

means drilling is missing at the point focused in

image.

Figure 5: pin hole

An open circuit defect is illustrated in fig 6 which

will highlight the breakage in the conductive path

Generative AI-Driven PCB Defect Detection and Classiﬁcation System

185

thereby circuit will not get closed and electricity will

not flow through the circuit.

Figure 6: open circuit

Fig 7 shows the short circuit defect is due to the

unintended connection between two separate

conductive paths. Excessive current will flow the

copper track and the track get may damage.

Figure 7: short circuit defect

Fig 8 shows the spur defect, in this an extra

protruding conductor that shouldn't be there which

can result in short circuit if in contact with other PCB

elements.

Figure 8: spur defect

An excessive copper defect is illustrated in fig 9

in this an unwanted copper left on the PCB after

etching, it causes heating and damage to the circuit.

Figure 9: excessive copper defect

Fig 10 shows the mouse bite defects in this a small

chunk of material missing from the edge of the PCB,

resembling a "bite." mouse bite are capable of causing

the performance of the PCB.

Figure 10: mouse bite defect

5 RESULT ANALYSIS

For testing purposes, single sided PCB images is

taken for consideration and data set generated and

tested under the proposed system. Following are the

identified defects

(Verma

and

Kumar

2021).

5.1 PCB Without Defects:

Figure 11 shows the sample PCB to be tested. The

system successfully captured high-resolution images

of defect-free PCBs using image acquisition methods.

Pre-processing of the images improves the quality of

images and achieves a remarkable 99% accuracy in

the identification of boards without defects,

indicating that the system is highly reliable in

validating integrity of the PCBs before production.

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Figure 11: PCB Without Defect

5.2 Detection of Defect PCB:

In fig 12 we improved the image preprocessing

techniques to improve the quality of images captured

and thus image clarity. The system was able to detect

defects, such as scratches and misalignment with an

average detection accuracy of 95%. This means quick

responses and thereby less production time is lost due

to delays.

Figure 12: Defected PCB under test (Verma and Kumar,

2021)

5.3 Detection And Identification of

Defect PCB:

In fig 13 for pinhole detection our proposed system

carried out specific image analysis in the pre-

processing stage, and so made it more sensitive. We

have classified 90% accuracy for the pinhole defects.

It allows the manufacturers to take immediate

measures towards small critical flaws, ensuring high

quality PCB production.

Figure 13: Pin hole defect detection

6 CONCLUSIONS

In summary, our proposed system proves to be

impressive in successfully identifying the defects

identified in AI based PCB inspection. Our system is

effective in catching 99% of defect-free boards with

almost the guarantee of a reliable preproduction

validation process. By refining techniques in image

preprocessing, we attained perfect defect detection

accuracy for defects such as scratches and

misalignments that minimize downtime in

production. Such analysis helps a manufacturer

quickly identify the critical issues in pinholes, with an

accuracy of around 90%. All such improvements

create enough assistance to a market leader in

maintaining quality PCB productions along with

frictionless manufacturing processes.

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