Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

Haq, Mohd Anul; Jilani, Abdul Khadar; Prabu, P.

doi:10.32604/cmc.2022.017698

Cited by 10 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods commonly use algorithms such as morphological methods, edge detection [14], Hough transform [15] and Fourier shape descriptors to describe the boundary characteristics of objects. Most existing PCB defect-detection methods achieve defect localization through subtraction or XOR operations [1,16] to find out the shape differences between two images. By analyzing PCB functional defects, we found that they can be further regarded as anomalies of the edge.…”

Section: Related Workmentioning

confidence: 99%

“…A method proposed to locate defects by finding the differences between the barycenter-edge points distance sequences of the template and test image using the circular correlation theorem. However, it [1,16] relies too heavily on the centroid calculation which can be easily influenced by defect contour points, leading to defect-detection errors and poor anti-interference ability.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

EEDD: Edge-Guided Energy-Based PCB Defect Detection

Deng

Sun

et al. 2023

Electronics

View full text Add to dashboard Cite

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We focus on the salient edge characteristic of PCB images and regard the functional defects as contour differences and then propose a novel energy measurement method for PCB contour anomaly detection. We introduce the energy transformation using the edge information of the template and test image, then Speeded-Up Robust Features method (SURF) is used for image alignment, and finally achieve defect detection by measuring the energy anomaly score pixel by pixel with the proposed energy-based defect localization and contour flood fill methods. Our method excels in detecting multi-scale defects, particularly tiny defects, and is robust against interferences such as non-finely aligned images and edge spurs. Experiments on the DeepPCB-A dataset and our high-resolution PCB dataset (HDPCB) show that the proposed method outperforms state-of-the-art methods in PCB defect-detection tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

EEDD: Edge-Guided Energy-Based PCB Defect Detection

Deng

Sun

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Regarding the traditional image-processing detection methods, Mukesh Kumar et al proposed a method to detect PCB bare board defects by combining image enhancement techniques and a standard template generation particle analysis [5]. Khlong Luang and Pathum Thani proposed a PCB defect classification method by using arithmetic and logical operations, a circular hough transform (CHT), morphological reconstruction (MR), and connected component labelling (CCL) [6]. Liu and Qu used a hybrid recognition method of mathematical morphology and pattern recognition to process PCB images and used an image aberration detection algorithm to mark images of PCB defects for defect identification [7].…”

Section: Introductionmentioning

confidence: 99%

YOLO-MBBi: PCB Surface Defect Detection Method Based on Enhanced YOLOv5

Wan

Lei

et al. 2023

Electronics

View full text Add to dashboard Cite

Printed circuit boards (PCBs) are extensively used to assemble electronic equipment. Currently, PCBs are an integral part of almost all electronic products. However, various surface defects can still occur during mass production. An enhanced YOLOv5s network named YOLO-MBBi is proposed to detect surface defects on PCBs to address the shortcomings of the existing PCB surface defect detection methods, such as their low accuracy and poor real-time performance. YOLO-MBBi uses MBConv (mobile inverted residual bottleneck block) modules, CBAM attention, BiFPN, and depth-wise convolutions to substitute layers in the YOLOv5s network and replace the CIoU loss function with the SIoU loss function during training. Two publicly available datasets were selected for this experiment. The experimental results showed that the mAP50 and recall values of YOLO-MBBi were 95.3% and 94.6%, which were 3.6% and 2.6% higher than those of YOLOv5s, respectively, and the FLOPs were 12.8, which was much smaller than YOLOv7’s 103.2. The FPS value reached 48.9. Additionally, after using another dataset, the YOLO-MBBi metrics also achieved satisfactory accuracy and met the needs of industrial production.

show abstract

TD‐YOLO: A Lightweight Detection Algorithm for Tiny Defects in High‐Resolution PCBs

Ling,

Isa

2023

Advcd Theory and Sims

View full text Add to dashboard Cite

Printing circuit board (PCB) defect inspection precisely and efficiently is an essential and challenging issue. Therefore, based on several improvements upon YOLOv5‐nano, a novel lightweight detector named TD‐YOLO is proposed to inspect tiny defects in PCBs. First, the lightweight ShuffleNet block is implemented into the backbone to effectively reduce the model weight. Second, novel anchors are designed using modified k‐means clustering to accelerate the model convergence and yield superior detection precision. Then, data augmentation strategy is recomposed by rejecting mosaic augmentation to suppress the emergence of extremely tiny targets. Finally, a mighty feature pyramid network namely MPANet, is newly proposed to boost the feature fusion capability of the model. The experiment results denote TD‐YOLO achieves the highest 99.5% mean average precision on our dataset, outperforming other state of the arts. Specially, the detection metrics for the smallest two defects, such as spur and mouse bite, are increased by 2.1% and 1.2%, respectively, compared with YOLOv5‐nano. Besides, TD‐YOLO has only 1.33 million parameters, decreased by 25% than the baseline. Using a mediocre processor, the detection speed is boosted by 20%, reaching 37 frames per second for the input size of 2240× 2240 pixels.

show abstract

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

Cited by 10 publications

References 16 publications

EEDD: Edge-Guided Energy-Based PCB Defect Detection

EEDD: Edge-Guided Energy-Based PCB Defect Detection

YOLO-MBBi: PCB Surface Defect Detection Method Based on Enhanced YOLOv5

TD‐YOLO: A Lightweight Detection Algorithm for Tiny Defects in High‐Resolution PCBs

Contact Info

Product

Resources

About