Defect Detection of Printed Circuit Boards Using EfficientDet

Jin, Jintao; Feng, Wei; Lei, Qujiang; Gui, Guangchao; Li, Xiuhao; Deng, Zhonghui

doi:10.1109/icsip52628.2021.9688801

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Further research should focus on investigating and building a robust SSL model for inspection systems to analyze multiple types of PCB defects and error resistance with higher proportions of noisy data. In addition, wider and deeper variants of WRN-28-2 and advanced models, such as Transformers, will be employed as the backbone to improve the capacity of the deep-learning model [ 35 , 36 , 37 ]. Furthermore, this study can be extended to classify various types of PCB [ 13 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

Pham

Thoi

Choi³

et al. 2023

Sensors

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Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.

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Section: Discussionmentioning

confidence: 99%

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

Pham

Thoi

Choi³

et al. 2023

Sensors

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“…Absolute Improv. % Evaluation value Evaluation method Reference map@0.5 = +10.43% map@0.5 = 88.2% map@50 [26] map@0.5 = +9.13% map@0.5 = 89.5% map@50 [27] map@0.5 = +0.73% map@0.5 = 97.9% map@50 [28] map@0.5 = +0.53% map@0.5 = 98.1% map@50 [29] APc = +0.89% APc = 97.74% APc = map@50 [9] F-measure = +2% Recall = 0% Precision = +2% map@50 = +2.33% F-measure = 96% Recall = 97% Precision = 96% map@50 = 96.3% F-measure Recall Precision map@50 [30] map@50 = +0.78% map@50 = 97.85% map@50 [31] map@50 = 98.63% Recall = 97% Precision = 98% F-measure = 98% map@50 Recall Precison F-measure Proposed method Table 5, shows a comparison between the results of the proposed method with PCB defect detection papers based on different datasets other than HRIPCB dataset. When comparing the accuracy of the proposed method with other state-of-the-art algorithms, it is evident that it has a good accuracy as these algorithms while solving the problem of the ICIoU loss function.…”

Section: Tablementioning

confidence: 99%

Valid-IoU: An Improved IoU-based Loss Function and Its Application to Detection of Defects on Printed Circuit Boards

Vakili

Karimian

Shoaran

et al. 2023

Preprint

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A crucial component of quality control during printed circuit board (PCB) production is defect detection. The PCB should be inspected during the manufacturing process in order to minimize defects such as printing errors, incorrect component selections, and incorrect soldering. Convolutional neural networks (CNNs) have become widely used due to their high recognition power. One of the factors affecting the accuracy of CNNs is loss function. Intersection over union (IoU) based loss function and its variants such as IoU, generalized IoU (GIoU), distance IoU (DIoU), complete IoU (CIoU), and improved CIoU (ICIoU) are common metrics for bounding box regression. The IoU-based loss functions, such as ICIOU, achieve remarkable success but still have some main drawbacks such as inaccurate regression. The ICIoU performance is degraded under conditions between the ratio of the corresponding height or width of the predicated bounding boxes and the ground truth bounding box. In this paper, an improved IoU called VIoU is introduced to solve this problem. By incorporating VIoU loss into state-of-the-art YOLOv4 object detection algorithm, an average accuracy of 98.63% on the PCB defect detection is achieved which is an improvement compared to existing IoU models.

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“…A defect detection method is based on the YOLOv5 [20][21][22] network with an enhanced perceptual field by introducing a coordinate attention mechanism and enhanced multiscale feature fusion. Based on the improved MobileNetv3 as the backbone feature extraction network, ECAnet [23] is introduced to adjust the feature weights adaptively to enhance the feature extraction of the network. Such defect detection methods have a small number of parameters and a relatively fast detection speed, but for small-target [24,25] defect detection, the feature extraction strength is insufficient and the detection accuracy is low.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Network DCR-YOLO for Surface Defect Detection on Printed Circuit Boards

Jiang,

Cai,

Zhang

2023

Sensors

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To resolve the problems associated with the small target presented by printed circuit board surface defects and the low detection accuracy of these defects, the printed circuit board surface-defect detection network DCR-YOLO is designed to meet the premise of real-time detection speed and effectively improve the detection accuracy. Firstly, the backbone feature extraction network DCR-backbone, which consists of two CR residual blocks and one common residual block, is used for small-target defect extraction on printed circuit boards. Secondly, the SDDT-FPN feature fusion module is responsible for the fusion of high-level features to low-level features while enhancing feature fusion for the feature fusion layer, where the small-target prediction head YOLO Head-P3 is located, to further enhance the low-level feature representation. The PCR module enhances the feature fusion mechanism between the backbone feature extraction network and the SDDT-FPN feature fusion module at different scales of feature layers. The C5ECA module is responsible for adaptive adjustment of feature weights and adaptive attention to the requirements of small-target defect information, further enhancing the adaptive feature extraction capability of the feature fusion module. Finally, three YOLO-Heads are responsible for predicting small-target defects for different scales. Experiments show that the DCR-YOLO network model detection map reaches 98.58%; the model size is 7.73 MB, which meets the lightweight requirement; and the detection speed reaches 103.15 fps, which meets the application requirements for real-time detection of small-target defects.

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Defect Detection of Printed Circuit Boards Using EfficientDet

Cited by 12 publications

References 23 publications

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

Valid-IoU: An Improved IoU-based Loss Function and Its Application to Detection of Defects on Printed Circuit Boards

Lightweight Network DCR-YOLO for Surface Defect Detection on Printed Circuit Boards

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