Generation of Pixel-Level Binary Masks for PCB Defects Dataset and Segmentation using U-Net

Defects in printed circuit boards (PCBs) are being detected using computer vision-based techniques. Defect-free PCBs are essential for the reliability of consumer electronics. However, deep learning-based methods often struggle with imbalanced defect distributions and limited generalization. To address these challenges, we propose ViT-Mamba, a hybrid framework that combines Vision Transformers with a Mamba-inspired attention mechanism for global feature extraction and precise defect segmentation. We further introduce an artificial defect generation module that systematically creates six types of PCB defects to improve robustness. A multiscale hierarchical refinement strategy is employed to enhance feature representation for accurate segmentation. Experiments on a public PCB defect dataset show that ViT-Mamba outperforms existing methods, achieving a mean Average Precision (mAP) of 99.69%.

This paper delves into various methodologies for enhancing the quality, design, and optimization of Printed Circuit Boards (PCBs). With the development of the technology, the importance of Printed Circuit Board(PCB) is recently mentioned by more and more people who is trying to make progress of human technology. The classification of PCB defects, a crucial step in ensuring product reliability, is discussed on several categories of defects in PCB, highlighting recent advancements such as fuzzy c-means segmentation, which significantly improves defect detection accuracy. When producing, the layout is quite significant. In a conference, a group of scientists used a thermal layout modelling and optimization routine to make the board having more space to put circuit on it. Methods to ensure PCB quality during production are examined, focusing on optical and electrical testing. The PCB design process is also explored, emphasizing the importance of those considerations before production. Finally, the paper reviews innovative approaches to PCB layout optimization, particularly in managing heat dissipation and dynamic behavior through advanced simulation techniques.

Printed Circuit Board (PCB) defect inspection is critical in electronics manufacturing, yet annotating large training sets is labor-intensive and costly. This paper proposes a semi-supervised PCB defect detection framework based on YOLOv5, which leverages a small labeled dataset and a larger pool of unlabeled images through an iterative self-training pipeline. A YOLOv5 detector is first trained on the limited labeled data, then used to generate pseudo-labels on unlabeled images; high-confidence detections are retained as defect annotations, and the model is retrained on the combination of labeled and pseudo-labeled data. This pseudo-labeling and retraining cycle is repeated for multiple iterations to progressively refine the detector. The approach is evaluated on a PCB defect dataset with 100 labeled and 1,000 unlabeled images, and shows significant gains over a fully supervised baseline. The proposed semi-supervised YOLOv5 achieves 91.6% mAP@0.5 with only 100 labeled images, outperforming both the baseline (87.0% mAP) and prior semi-supervised methods, while substantially improving recall and precision. The results demonstrate that the method effectively reduces annotation effort while maintaining high detection accuracy, providing a simple, confidence-thresholded self-training strategy for deploying PCB defect detectors under limited labeling resources. This work directly supports SDG 9 by enabling cost-efficient, high-accuracy AI-based PCB inspection that strengthens intelligent and sustainable manufacturing systems.

The manufacturing quality of printed circuit boards (PCBs) significantly influences the functionality and life expectancy of electronic devices. This paper introduces a YOLO-WWBi based on improved YOLO11 framework method for the detection of surface defects. First, an improved weighted and re-parameterized ghost multi-scale feature aggregation module (WRGMSFA) is designed. This module focuses more on defect information channels, enhancing multi-scale feature extraction capabilities while suppressing redundant information. Then, BiFPN is integrated into the neck to enhance the quality of fused features and deepen the interaction of feature information. Finally, the WIoU loss function was employed to optimize the localization performance of defect positions, thereby enhancing robustness in highly similar PCB background interference. The experimental results indicate that YOLO-WWBi has an mAP of 96.6%, surpassing YOLO11 by 5.4 points. Its performance metrics indicate that the requirements for the high-precision, real-time detection of PCB defects are satisfactorily met.

The small area of a printed circuit board (PCB) results in densely distributed defects, leading to a lower detection accuracy, which subsequently impacts the safety and stability of the circuit board. This paper proposes a new YOLO-BFRV network model based on the improved YOLOv8 framework to identify PCB defects more efficiently and accurately. First, a bidirectional feature pyramid network (BIFPN) is introduced to expand the receptive field of each feature level and enrich the semantic information to improve the feature extraction capability. Second, the YOLOv8 backbone network is refined into a lightweight FasterNet network, reducing the computational load while improving the detection accuracy of minor defects. Subsequently, the high-speed re-parameterized detection head (RepHead) reduces inference complexity and boosts the detection speed without compromising accuracy. Finally, the VarifocalLoss is employed to enhance the detection accuracy for densely distributed PCB defects. The experimental results demonstrate that the improved model increases the mAP by 4.12% compared to the benchmark YOLOv8s model, boosts the detection speed by 45.89%, and reduces the GFLOPs by 82.53%, further confirming the superiority of the algorithm presented in this paper.

In order to solve the problem of low accuracy and efficiency in printed circuit board(PCB) defect detection using reference methods, a Transformer-YOLO network detection model is proposed. Firstly, an imporved clustering algorithm is used to generate the anchor box suitable for the PCB defect data set of this paper. Secondly, abandoning the traditional idea of using convolutional neural network to extract image feature, Swin Transformer is used as the feature extraction network, which can effectively establish the dependency between image features. Finally, to modify the order of the channels in the feature map and enable the network to more effectively focus on the information with greater value, the convolution and attention mechanism module is added to the feature detection network component. Comparing the network model proposed in this paper with Faster R-CNN, SSD, YOLOv3, YOLOv4 and YOLOv5, the experimental results show that the proposed model improves the accuracy by 23.90%, 15.51%, 10.70%, 7.83% and 6.12% respectively, which is better than other most mainstream target detection models and has relatively small volume.

Aiming at the conflict between the defect feature recognition capability and the detection speed of current vision inspection techniques in the task of detecting tiny defects in printed circuit boards (PCBs). In this paper, we proposed an EPD-YOLO with a focus information transfer (FIT) structure and a structurally flexible head (SFHead). While improving the network’s ability to recognize tiny defects in similar PCBs through FIT, the feature information capturing capability of the dual SFHead structure is utilized to ensure detection accuracy and improve real-time detection speed. Experimental results show that the proposed EPD-YOLO has a mAP of 97.6 % , while the number of network parameters is only 5.13M, and it takes only 7.4 ms to detect an image, which achieves a better balance between accuracy and speed.

Printed Circuit Boards (PCBs) are fundamental components in modern electronic devices, and their quality directly affects product reliability. Conventional inspection processes often rely on supervised learning models that require a large amount of annotated defect data. However, collecting and labeling defect samples is costly and impractical due to the diverse and unpredictable nature of PCB defects. In this study, we propose an unsupervised learning-based defect detection framework that leverages PCB circuit images for automated quality inspection. The approach employs convolutional autoencoders to learn standard pattern representations without requiring labeled defect data. Anomalous regions are detected by reconstruction error analysis, and statistical thresholds are applied to classify pass/fail conditions. To evaluate the effectiveness, experiments were conducted on a dataset of PCB circuit images, including both standard and defective samples. The proposed method demonstrated high detection accuracy with minimal false positives, outperforming baseline thresholding and clustering-based approaches. Moreover, the framework showed robustness to varying defect types such as missing tracks, misalignments, and surface contamination. These results suggest that unsupervised learning can serve as a practical alternative to traditional supervised inspection methods in PCB manufacturing. The proposed method reduces dependency on labeled datasets, enhances adaptability to new defect patterns, and contributes to the realization of intelligent and automated quality inspection systems in smart manufacturing environments.

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB) manufacturing. Due to the fatigue and speed requirement, manual inspection is ineffective to inspect every printed circuit board. Hence, this paper presents an efficient algorithm for an automated visual PCB inspection system that is able to automatically detect and locate any defect on PCBs. The defect is detected by utilizing wavelet-based image difference algorithm. The coarse resolution defect localization algorithm, is also presented. The coarse resolution defect localization algorithm is applied to the coarse resolution differenced image in order to locate the defective area on the fine resolution tested PCB image. In addition, the performance of the algorithm is evaluated to verify the efficiency of the proposed algorithm in term of computation time. This new method turned out to be computationally less intensive than traditional image difference operation. One conclusion from this paper is that the second level Haar wavelet transform should be chosen for the application of automated visual PCB inspection.

PurposeTo present and discuss open trace defects uncovered in an FR4 assembly during electrical testing.Design/methodology/approachThis paper presents open trace defects observed in FR4 assemblies and analyses the distribution of defects. The paper also discusses possible root causes for their occurrence.FindingsThe open trace defects that occurred during printed circuit board (PCB) fabrication should have been observed by the board manufacturer. It appears that the PCB manufacturer did not perform automatic optical inspection (AOI) and electrical testing during the manufacturing of the boards. The cost due to the rejected PCBAs was approximately 3x times that of the PCB cost.Originality/valueThe paper highlights the costly impact of uncovering a PCB defect after assembly. Based on the results of this study, the implementation of electrical testing and AOI for PCBs is recommended.

This paper presents inspection of Printed Circuit Boards (PCBs) based on normalized crosscorrelation. Correlation gives the similarity measure of images. In this paper, Normalized Cross-Correlation has been used to differentiate between a defective and defect free printed circuit board. Different PCBs have been inspected using normalized cross-correlation in this paper and further defected PCBs have been used for detection of all possible defects. Image subtraction(one of referential methods) is used for detection of defects of PCBs. And after detection of defects using image subtraction method, the wrong hole defects, missing conductors defects, etching defects, break lines have been classified.

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This paper describes an automatic defect segmentation method using U-Net based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. Performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This work demonstrates that, using XCT images, U-Net can be effectively applied for automatic segmentation of AM porosity with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.

As the application of power electronic components in renewable and green energy technologies continues to increase, there is an increasing trend in the utilization of higher voltage and frequency levels. This shift may result in more frequent partial discharge (PD) phenomena that can accelerate the degradation of insulation materials and shorten the lifespan of the components. Therefore, it is critical to ensure the reliability and durability of these components. This study investigated the effects of voltage magnitude and frequency on the PD and insulation degradation of printed circuit board (PCB) samples. Five different test samples were fabricated using a PCB as the substrate and an epoxy resin as the insulating coating. Two distinct testing configurations were employed: one for assessments based on a commercial frequency (60 Hz) and the other for assessments based on a high frequency (600 Hz). This study examined the behavior of PD within PCBs and the resulting insulation deterioration. The results demonstrated that all the samples exhibited significantly lower PD inception voltages (PDIV) at high frequencies compared with those at the commercial frequency. Specifically, the maximum decrease in the PDIV observed was 5 kV, while the minimum reduction was 3.3 kV. These findings highlight the critical impact of frequency on the PD behavior and underscore the importance of understanding these effects to enhance the reliability of electronic components in high-frequency applications.

Printed circuit board (PCB) defect detection is essential for ensuring the reliability of electronic manufacturing. Traditional automated optical inspection (AOI) methods rely on handcrafted features and rule-based heuristics, which often fail under complex defect patterns. Recent progress in deep learning, particularly the YOLO family of object detectors, has significantly improved industrial inspection. This paper presents a systematic benchmark of the YOLOv12 series, including five model scales (n, s, m, l, x), for PCB defect detection. All variants are trained and evaluated under unified protocols on a standard PCB dataset. Results show that YOLOv12l and YOLOv12x achieve the highest accuracy (mAP@0.5 ≈ 0.88 – 0.89), while YOLOv12n and YOLOv12s provide superior inference speed ( inference-only >300 FPS, end-to-end 6–8 FPS) at the expense of precision. YOLOv12m emerges as the most balanced variant, offering ~0.92 mAP@0.5 with real-time throughput. Error analysis using precision – recall curves and confusion matrices highlights challenges in fine-grained categories such as spur and mouse_bite. These findings provide practical guidelines for selecting YOLOv12 variants in PCB inspection, balancing accuracy and efficiency for diverse deployment scenarios.

Printed circuit boards (PCBs) are ubiquitous and essential electronic components. Tiny targets and high precision are the focus of PCB defect detection. This paper proposes an improved model focusing on tiny defect detection and model compression to achieve better performance in PCB defect detection. The improved model has a compact structure based on MobileNet v3 Small-CA and an image-cutting layer. Moreover, it applies an improved multi-scale fusion step with a location weighted mechanism to enhance representation performance. The proposed model outperforms state-of-the-art detection algorithms such as Faster R-CNN, EfficientDet, SSD, and YOLO v7, based on experimental results on a public synthetic PCB dataset. The proposed tiny object detection model has better performances on speed and detection accuracy, thereby benefitting the manufacturing industries associated with PCBs.