Printed Circuit Board Defect Detection Research Articles

Attentive context and semantic enhancement mechanism for printed circuit board defect detection with two-stage and multi-stage object detectors

Printed Circuit Boards (PCBs) are key devices for the modern-day electronic technologies. During the production of these boards, defects may occur. Several methods have been proposed to detect PCB defects. However, detecting significantly smaller and visually unrecognizable defects has been a long-standing challenge. The existing two-stage and multi-stage object detectors that use only one layer of the backbone, such as Resnet’s third layer (C4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C_4$$\\end{document}) or fourth layer (C5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C_5$$\\end{document}), suffer from low accuracy, and those that use multi-layer feature maps extractors, such as Feature Pyramid Network (FPN), incur higher computational cost. Founded by these challenges, we propose a robust, less computationally intensive, and plug-and-play Attentive Context and Semantic Enhancement Module (ACASEM) for two-stage and multi-stage detectors to enhance PCB defects detection. This module consists of two main parts, namely adaptable feature fusion and attention sub-modules. The proposed model, ACASEM, takes in feature maps from different layers of the backbone and fuses them in a way that enriches the resulting feature maps with more context and semantic information. We test our module with state-of-the-art two-stage object detectors, Faster R-CNN and Double-Head R-CNN, and with multi-stage Cascade R-CNN detector on DeepPCB and Augmented PCB Defect datasets. Empirical results demonstrate improvement in the accuracy of defect detection.

FSPDD: A double-branch attention guided network for few-shot PCB defect detection

AbstractDuring the production of printed circuit board (PCB), there will be defects due to inappropriate operations, which will affect the use of electronic products. Majority defect detection methods cost a large number of annotated samples to train detection models. However, PCB defect samples are difficult to collect. Moreover, existing few-shot object detection methods tend to extracting low-level features from support and query images via the shared backbone such as ResNet-50. However, it is not sufficient to obtain fine-grained prior guidance. To address the above issues, we propose a few-shot PCB defect detection model with double-branch attention. Specifically, the joint attention enhancement (JAE) module is proposed to fully mine effective information of query PCB images in multiple dimensions to enhance the representation of latent defects. Then, the multi-scale guidance (MSG) module is proposed to integrate prior knowledge within support PCB images into vectors to reweight query PCB images. Experiments on the PCB defect dataset demonstrate that AP of FSPDD outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30) and our proposed FSPDD has a good generalization ability, in which AP reachs 0.273 when $$k=30$$ k = 30 and is 5.28% higher than SOTA methods.

Enhancing YOLOv5 for superior PCB defect detection: lightweight heads and small target precision

Abstract This paper introduces an improved YOLOv5 network designed to achieve enhanced performance in the field of printed circuit board (PCB) defect detection. We incorporate a lightweight detection head, reducing the number of network parameters and improving computational efficiency. This not only facilitates operation in resource-constrained environments but also maintains detection accuracy. To better address the specific requirements of PCB defect detection, we introduce a small target detection head. By adding a detection head specifically designed for small targets, we enhance the network’s ability to perceive subtle defects, thereby improving detection accuracy. Finally, we validate our proposed improvements through experiments on a large-scale PCB defect dataset. Compared to the traditional YOLOv5 network, our model exhibits significant improvements in both accuracy and efficiency.

EHIR: Energy-based Hierarchical Iterative Image Registration for Accurate PCB Defect Detection

Printed Circuit Board (PCB) Surface defect detection is crucial to ensure the quality of electronic products in manufacturing industry. Detection methods can be divided into non-referential and referential methods. Non-referential methods employ designed rules or learned data distribution without template images but are difficult to address the uncertainty and subjectivity issues of defects. In contrast, referential methods use templates to achieve better performance but rely on precise image registration. However, image registration is especially challenging in feature extracting and matching for PCB images with defective, reduplicated or less features. To address these issues, we propose a novel Energy-based Hierarchical Iterative Image Registration method (EHIR) to formulate image registration as an energy optimization problem based on the edge points rather than finite features. Our framework consists of three stages: Edge-guided Energy Transformation (EET), EHIR and Edge-guided Energy-based Defect Detection (EEDD). The novelty is that the consistency of contours contributes to aligning images and the difference is highlighted for defect location. Extensive experiments show that this method has high accuracy and strong robustness, especially in the presence of defect feature interference, where our method demonstrates an overwhelming advantage over other methods.

Automatic PCB Sample Generation and Defect Detection Based on ControlNet and Swin Transformer.

In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication, we propose a sample generation method using Stable Diffusion Model and ControlNet, as well as a defect detection method based on the Swin Transformer. The method consists of two stages: First, high-definition original images collected in industrial production and the corresponding prompts are input to Stable Diffusion Model and ControlNet for automatic generation of nonindependent samples. Subsequently, we integrate Swin Transformer as the backbone into the Cascade Mask R-CNN to improve the quality of defect features extracted from the samples for accurate detection box localization and segmentation. Instead of segmenting individual components on the PCB, the method inspects all components in the field of view simultaneously over a larger area. The experimental results demonstrate the effectiveness of our method in scaling up nonindependent sample datasets, thereby enabling the generation of high-quality datasets. The method accurately recognizes targets and detects defect types when performing multitarget inspection on printed circuit boards. The analysis against other models shows that our improved defect detection and segmentation method improves the Average Recall (AR) by 2.8% and the mean Average Precision (mAP) by 1.9%.

DSASPP: Depthwise Separable Atrous Spatial Pyramid Pooling for PCB Surface Defect Detection

Printed circuit board (PCB) defect detection is an important and indispensable part of industrial production. PCB defects, due to the small target and similarity between classes, in the actual production of the detection process are prone to omission and false detection problems. Traditional machine-learning-based detection methods are limited by the actual needs of industrial defect detection and do not show good results. Aiming at the problems related to PCB defect detection, we propose a PCB defect detection algorithm based on DSASPP-YOLOv5 and conduct related experiments on the PKU-Market-PCB dataset. DSASPP-YOLOv5 is an improved single-stage detection model, and we first used the K-means++ algorithm for the PKU-Market-PCB dataset to recluster the model so that the model is more in line with the characteristics of PCB small target defects. Second, we design the Depthwise Separable Atrous Spatial Pyramid Pooling (DSASPP) module, which effectively improves the correlation between local and global information by constructing atrous convolution branches with different dilated rates and a global average pooling branch. The experimental results show that our model achieves satisfactory results in both the mean average precision and detection speed metrics compared to existing models, proving the effectiveness of the proposed method.

LW-YOLO: Lightweight Deep Learning Model for Fast and Precise Defect Detection in Printed Circuit Boards

Printed circuit board (PCB) manufacturing processes are becoming increasingly complex, where even minor defects can impair product performance and yield rates. Precisely identifying PCB defects is critical but remains challenging. Traditional PCB defect detection methods, such as visual inspection and automated technologies, have limitations. While defects can be readily identified based on symmetry, the operational aspect proves to be quite challenging. Deep learning has shown promise in defect detection; however, current deep learning models for PCB defect detection still face issues like large model size, slow detection speed, and suboptimal accuracy. This paper proposes a lightweight YOLOv8 (You Only Look Once version 8)-based model called LW-YOLO (Lightweight You Only Look Once) to address these limitations. Specifically, LW-YOLO incorporates a bidirectional feature pyramid network for multiscale feature fusion, a Partial Convolution module to reduce redundant calculations, and a Minimum Point Distance Intersection over Union loss function to simplify optimization and improve accuracy. Based on the experimental data, LW-YOLO achieved an mAP0.5 of 96.4%, which is 2.2 percentage points higher than YOLOv8; the precision reached 97.1%, surpassing YOLOv8 by 1.7 percentage points; and at the same time, LW-YOLO achieved an FPS of 141.5. The proposed strategies effectively enhance efficiency and accuracy for deep-learning-based PCB defect detection.

Printed Circuit Board Defect Detection Algorithm Based on yolov5s Structure Improvement

In terms of the existing PCB defect detection algorithm in complicated model structure with huge numbers of parameters slow speed, low detection accuracy and other problems, an improved PCB defect detection algorithm which is based upon yolov5s is proposed. Taking the yolov5s model as the main framework, the Omni-Dimensional Dynamic Convolution (OdConv) structure is used to replace the backbone of the original model, and the Convolutional Block Attention Module (CBAM) attention mechanism is introduced after the Conv layer of the Neck network. CBAM attention mechanism is brought in after the Conv layer of the Neck network (Neck), and the Focal-EIoU Loss function is modified by the Complete Intersection and Merger Ratio Loss (CIoU Loss) function, and different weights are assigned to high-quality samples and low-quality samples respectively, which solves sample imbalance existing in the target detection problem, and the results show that this algorithm is comparable to the original model. Experiments are conducted and the results show that this paper’s algorithm improves the mAP by 1.9%, speeds up by 47.75Fps, decrease the model size by 13.26 M, and reduces the computation volume by 12.3 G compared to yolov5s at IoU = 0.5.

Enhanced Printed Circuit Board defect detection through Adaboost Classifier Integration

Enhanced Printed Circuit Board defect detection through Adaboost Classifier Integration

PCB Board Defect Detection Method based on Improved YOLOv8

This study provides an improved YOLOv8-based printed circuit board (PCB) defect identification method to address the current challenges associated with PCB defect detection, including the detection of small targets, low accuracy, and other related concerns. The YOLOv8 model serves as the foundational framework, and in order to enhance detection speed, the YOLOv8s model is selected due to its reduced parameter count. However, feature extraction becomes challenging for small target defects; to address this, the CA attention mechanism is implemented, which is more attuned to target feature information and aids in feature extraction. As indicated by the experimental findings, the enhanced YOLOv8s-CA algorithm model has the following characteristics: a footprint of 5.79 MB, a mean average precision (mAP) of 90.4 percent, an increase of 6.6 percent over the initial network, and a parameter count augmentation of merely 0.007M. Consequently, this model finds utility in compact industrial inspection apparatus and possesses a wide range of potential applications.

LDD-Net: Lightweight printed circuit board defect detection network fusing multi-scale features

The current printed circuit board (PCB) defect detection model is difficult to balance accuracy and computational cost and cannot satisfy the requirements of practical applications. In this regard, a lightweight PCB image defect detection network (LDD-Net) with high accuracy is proposed in this study. First, LDD-Net designs a novel lightweight feature extraction network (LFEN), which adds convolutional layers for extracting large-scale feature maps and uses an efficient downsampling method to extract richer defect features. LFEN not only greatly reduces resource requirements, but also improves detection accuracy. Second, LDD-Net devises multi-scale aggregation network (MAN) to facilitate information sharing among feature maps at different scales. In addition, MAN incorporates an efficient attention module to suppress similar background interference. Finally, LDD-Net uses the lightweight decoupling head to detect three groups of shallow feature maps with rich information of the small object. Experimental results on the PCB defect dataset ,and VisDrone public dataset show that the mean average precision (mAP), recall, and precision of LDD-Net outperform state-of-the-art lightweight object detection models. The mAP, computation, number of parameters, and detection speed of LDD-Net are 95.90%, 21.48 G, 5.13 M, and 78.13 frames per second, respectively. LDD-Net effectively balances accuracy, computational cost, and detection speed for applications in edge systems or resource-constrained embedded devices.

A Novel PCB Defect Detection Method Based on Digital Image Processing

Printed circuit board (PCB) has a wide range of applications, and its automated quality inspection is a vital part of economic production. The time overhead of the current PCB defect detection method will increase with the increase in the number of detection targets. To ensure the stability of detection performance, this paper proposes a novel PCB detection method based on color threshold segmentation. Experiments show that this method can effectively detect and analyse various information about solder pads and solder pastes, among which the detection accuracy rate of solder pads is 99.4%, the missed detection rate is 0.4%, and the detection accuracy rate of solder paste is 99.3%, The missed detection rate is 0.03%. At the same time, the method in this paper has better robustness and stability than other PCB defect detection methods, and can better meet the needs of industrial production.

A Design of Bare Printed Circuit Board Defect Detection System Based on YOLOv8

As electronic products develop towards miniaturization and digitization, printed circuit boards (PCBs) also develop towards high density and high precision. In the manufacturing process of PCBs, some PCBs with defects will be produced, and these defects often lead to circuit failure, so defect detection technology is an indispensable part of PCB manufacturing technology. Aiming at the problems of low efficiency and accuracy of traditional image recognition and classification technology, A PCB defect detection algorithm is proposed based on YOLOv8 in this paper. For these five PCB defects, the neural network in deep learning was used to identify and classify PCB defects. The prediction accuracy of YOLOv8 model after training is close to 97%, and it is compared with the accuracy of other algorithms to prove the effectiveness and feasibility of the model. In addition, the object detection image user interface is also established in this paper, which can realize image detection more conveniently.

Hybrid pooling and channel attention‐based single‐shot detector for printed circuit board defect detection

AbstractAutomatic defect detection is a key but challenging technology for managing printed circuit board (PCB) production quality. In recent years, deep neural networks (DNNs) have attracted considerable attention for PCB defect detection. However, due to the complexity, diversity, and small‐scale characteristics of defects, it remains a challenge. In this letter, a single‐shot detector (SSD)‐based defects detection method is proposed. This method treats the test and template image as a single image with two channels. In order to reduce the information loss caused by the traditional pooling methods, a new pooling method combining three types of pooling features is proposed. To further improve the detection performance, the channel attention (CA) mechanism is introduced into the detection network. Our proposed method achieves 99.64% mean average precision (mAP) on the DeepPCB dataset, which surpasses the state‐of‐the‐art methods.

EEDD: Edge-Guided Energy-Based PCB Defect Detection

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.

A deep context learning based PCB defect detection model with anomalous trend alarming system

The quality of a printed circuit board (PCB) is paramount towards ensuring proper functionality of electronic products. To achieve the required quality standards, substantial research and development efforts were invested to automate PCB inspection for defect detection, primarily using computer vision techniques. Despite these advancements, the accuracy of such techniques is often susceptible towards varying board and component size. Efforts to increase its accuracy especially for small or tiny defects on a PCB often lead to a tradeoff with reduced real-time performance, which in turn limits its applicability in the manufacturing industry. Hence, this paper puts forward an enhanced deep learning network which addresses the difficulty in inferring tiny or varying defects on a PCB in real-time. Our proposed enhancements consist of i) A novel multi-scale feature pyramid network to enhance tiny defect detection through context information inclusion; and ii) A refined complete intersection over union loss function to precisely encapsulate tiny defects. Experimental results on a publicly available PCB defects dataset demonstrate that our model achieves 99.17% mean-average precision, while maintaining real-time inferencing speed at 90 frames per second. In addition, we introduce three trend detection algorithms which alert an operator when abnormal development of defect characteristics is detected. Each algorithm is responsible for localizing defect buildups, increasing defect size and increasing defect occurrences, respectively. As a whole, the proposed model is capable of performing accurate and reliable real-time PCB inspection with the aid of an automated alert capability. The dataset and trained models are available at: https://github.com/JiaLim98/YOLO-PCB.

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Printed circuit boards (PCBs) are a nearly ubiquitous component of every kind of electronic device. With the rapid development of integrated circuit and semiconductor technology, the size of a PCB can shrink down to a very tiny dimension. Therefore, high-precision and rapid defect detection in PCBs needs to be achieved. This paper reviews various defect detection methods in PCBs by analysing more than 100 related articles from 1990 to 2022. The methodology of how to prepare this overview of the PCB defect detection methods is firstly introduced. Secondly, manual defect detection methods are reviewed briefly. Then, traditional image processing-based, machine learning-based and deep learning-based defect detection methods are discussed in detail. Their algorithms, procedures, performances, advantages and limitations are explained and compared. The additional reviews of this paper are believed to provide more insightful viewpoints, which would help researchers understand current research trends and perform future work related to defect detection.

Intelligent Print Circuit Board Defect Detection

During the manufacturing process of electronic products, one of the most critical stages is the soldering of components onto the printed circuit board (PCB). Even a minor defect in the PCB can lead to significant issues in the final product. Therefore, a rigorous\ndefect inspection process is employed during manufacturing, which can be categorized into manual inspection and automated optical inspection (AOI). Manual inspection suffers from drawbacks such as slow speed and the expenditure of manpower and costs. Hence, most manufacturers opt for automated optical inspection to expedite production. However, most current automated optical inspection systems rely on traditional optical inspection algorithms. These computational methods are susceptible to variations in lighting conditions caused by slight differences in the placement of PCBs or the amount of solder. Consequently, these variations often result in misjudgments, where qualified PCBs are mistakenly categorized as defective, leading to high false positive rates in AOI systems.\nThis paper presents a deep learning based method for PCB defect detection. The proposed approach involves the creation of two separate models for classifying defective components individually. Once both models demonstrate a basic recognition capability, they are combined into a master model using the method proposed in this study to enhance overall recognition accuracy. The model has\nbeen trained on datasets for capacitors and resistors, and the experimental results indicate an accuracy of over 99% for both component types.

Detection Algorithm of Surface Defect Word on Printed Circuit Board

For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detection methods. In order to further meet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the You-only-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is preserved for PCB defect feature information extraction, and secondly, the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process. Then, the K-means++ clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection, which further improves the recognition and localization of small PCB defects. Finally, the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms. The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%, which has better detection capability, lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.

PCB defect detection system based on image processing

Under the background of intelligent manufacturing, the quality inspection of printed circuit board (PCB), as the carrier of electronic products, plays an important role. In view of the shortcomings of existing traditional detections, this paper has put forward a PCB detection method based on machine vision, focusing on image preprocessing algorithm and detection analysis. The median filter algorithm was used to remove the noise in the image. Logarithmic transformation was used to enhance image contrast. Sobel operator was used for edge detection to obtain the geometric center of the defect area. On this basis, the PCB image was registered by template matching method, and the difference between the preprocessed PCB image to be tested and the standard PCB image was calculated to get the difference map. Subsequently, morphological algorithm was used to remove the slight differences and obtain the defect map. In the end, the simulation results have shown that the proposed algorithm has good detection effect and high precision in PCB defect detection.