A New Based Image Subtraction Algorithm for Bare PCB Defect Detection

Bonello, Daniel Katz; Iano, Yuzo; Neto, Umberto Bonello

doi:10.20533/ijmip.2042.4647.2018.0054

Cited by 8 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Benello et al explore the application of image subtraction methods for detecting bare PCB layouts' failures. The researchers encompass developing a primary subtraction algorithm and assessing its efficacy using MATLAB simulations [8]. The findings by Benello et al leads to the expansion of automated PCB defect detection since the studies propose algorithms aimed at improving the accuracy, speed, and detection of the various defects types.…”

Section: The Defect Detection Methods Of Pcb Componentsmentioning

confidence: 99%

Circuit defect detection based on AI deep learning

Wang

2023

TNS

View full text Add to dashboard Cite

In the milieu of promptly advancing technology and increasing demand for electronic devices, circuit defect detection has become crucial to warranting product quality. This study tackles the cons of traditional defect detection methods, proposing a mind-boggling approach based on AI deep learning. The study intends to establish and enhance deep learning algorithms for the exact and real-time detection of circuit defects. This research encompasses an in-depth review of existing literature on circuit defect detection and AI deep learning, underlining the existing gaps and pitfalls in the field. The study will primarily deploy convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as the primary tools to process various data modalities. The results highlight that the proposed AI deep learning framework depicts grander performance, unlike in traditional manual inspection. The study sets precedence in AI applications in quality control as it contributes to improved manufacturing efficiency, reduced production costs, and delivery of utmost-quality electronic products to consumers.

show abstract

Section: The Defect Detection Methods Of Pcb Componentsmentioning

confidence: 99%

Circuit defect detection based on AI deep learning

Wang

2023

TNS

View full text Add to dashboard Cite

show abstract

“…The ability to distinguish between spatter-like structures and other signals is crucial for monitoring zinc splatter, and the results from (Nettekoven et al, 2022) suggest that the mentioned algorithms may be beneficial. The MOG algorithm was applied in Bonello et al (2020) for inspection of missing and misaligned components in printed circuit board assemblies (PCBAs). The results showed the algorithm was capable at distinguishing between reference PCBA images and a defective PCBA images, highlighting the effectiveness of the method (Bonello et al, 2020).…”

Section: Background Subtraction Applicationsmentioning

confidence: 99%

“…The MOG algorithm was applied in Bonello et al (2020) for inspection of missing and misaligned components in printed circuit board assemblies (PCBAs). The results showed the algorithm was capable at distinguishing between reference PCBA images and a defective PCBA images, highlighting the effectiveness of the method (Bonello et al, 2020). Whilst the study in Bonello et al ( 2020) is innovative and effective, it focuses on still images and essentially performs anomaly detection for quality control.…”

Section: Background Subtraction Applicationsmentioning

confidence: 99%

Real-time monitoring of molten zinc splatter using machine learning-based computer vision

O’Donovan,

Giannetti,

Pleydell-Pearce

2024

J Intell Manuf

View full text Add to dashboard Cite

During steel galvanisation, immersing steel strip into molten zinc forms a protective coating. Uniform coating thickness is crucial for quality and is achieved using air knives which wipe off excess zinc. At high strip speeds, zinc splatters onto equipment, causing defects and downtime. Parameters such as knife positioning and air pressure influence splatter severity and can be optimised to reduce it. Therefore, this paper proposes a system that converges computer vision and manufacturing whilst addressing some challenges of real-time monitoring in harsh industrial environments, such as the extreme heat, metallic dust, dynamic machinery and high-speed processing at the galvanising site. The approach is primarily comprised of the Counting (CNT) background subtraction algorithm and YOLOv5, which together ensure robustness to noise produced by heat distortion and dust, as well as adaptability to the highly dynamic environment. The YOLOv5 element achieved precision, recall and mean average precision (mAP) values of 1. When validated against operator judgement using mean average error (MAE), interquartile range, median and scatter plot analysis, it was found that there was more discrepancy between the two operators than the operators and the model.This research also strategises the deployment process for integration into the galvanising line. The model proposed allows real-time monitoring and quantification of splatter severity which provides valuable insights into root-cause analysis, process optimisation and maintenance strategies. This research contributes to the digital transformation of manufacturing and whilst solving a current problem, also plants the seed for many other novel applications.

show abstract

“…In 2018, Bonello et al [8] presented an image subtraction technique to detect defects in bare PCBs. Tsai and Huang [9] presented an automatic defect detection method based on template matching and used a global Fourier image reconstruction method to detect and localize minor defects in images with nonperiodic patterns.…”

Section: Related Workmentioning

confidence: 99%

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

Niu

Chen

et al. 2023

Journal of Sensors

View full text Add to dashboard Cite

With the rapid development of China’s printed circuit board industry, bare-board defect detection has high research and application values as an important factor in improving production quality. In this paper, a new detection method based on YOLOv5 is proposed to solve the balance problem of efficiency and performance in the task of circuit board defect detection. First, the k -means++ method is used to improve the location matching of the prior anchor boxes. Second, the Focal-EIOU loss function is used instead of GIOU to address the former’s degeneration issue. Finally, the ECA-Net module is added to enhance the sensitivity of the model to channel features. Experiments were conducted on a public defect dataset, and superior performance was achieved. The proposed method achieves 99.1% mean average precision at 86 frames per second. Compared with other advanced methods, the proposed method achieves the highest mean average precision value, and the detection speed allows real-time applications.

show abstract

A New Based Image Subtraction Algorithm for Bare PCB Defect Detection

Cited by 8 publications

References 5 publications

Circuit defect detection based on AI deep learning

Circuit defect detection based on AI deep learning

Real-time monitoring of molten zinc splatter using machine learning-based computer vision

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

Contact Info

Product

Resources

About