Defect detection of printed circuit board based on lightweight deep convolution network

Shen, Jiaquan; Liu, Ningzhong; Sun, Han

doi:10.1049/iet-ipr.2020.0841

Cited by 32 publications

(22 citation statements)

References 38 publications

(46 reference statements)

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“…The proposed method is capable to perform the detection, location and orientation checking of resistors. The average processing time of a single resistor is 2.66 − 4.29s.Shen et al [7] have established a lightweight deep convolution network detection model. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production.…”

Section: Related Workmentioning

confidence: 99%

Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Jiang

Zhang

et al. 2023

SIViP

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Defect detection is a critical element in the PCB manufacturing process. Different from surface mount PCB, the components on the plug-in PCB are usually installed manually, resulting in significant errors. Furthermore, because plug-in components have many different types and irregularities, it is difficult to detect their flaws. We make contributions in the following two aspects: (1) a framework and measurement method of a light source and make a cheap and efficient lighting system; (2) a fusion algorithm based on machine learning and morphology for polarity detection of plug-in capacitors. The capacitor is detected using SVM and fused with the polar coordinate expansion method. The AOI system and the proposed fusion algorithm have been applied to the production line, with an accuracy of 99.73% and a missed detection rate of only 0.12%, according to the field test of the production line.

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Section: Related Workmentioning

confidence: 99%

Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Jiang

Zhang

et al. 2023

SIViP

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“…The technique includes an object network extrusion and excitation (SENet) module, a channel feature fusion module, a feature pyramid network (FPN) module, and a ROI network module, which dramatically improves defect detection performance of CNN. A network model for PCB detection was presented by Shen Jiaquan et al [20]. This model is named LD-PCB, the traditional model is difficult to handle complex components and a wide variety of problems are solved by LD-PCB.…”

Section: A Defect Detection Based On Cnnmentioning

confidence: 99%

Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm

et al. 2022

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In the process of production, automobile steel forgings are prone to various cracks, which affect the product quality. At present, forgings defects are mainly detected by fluorescent magnetic particle inspection and manual inspection. Aiming at the problems of low detection accuracy and efficiency in this method, an improved convolutional neural network model is proposed. The fluorescent magnetic particle inspection images of two typical forgings were intelligently inspected. Firstly, a deep learning model with EfficientNet as the backbone and Feature Pyramid Network (FPN) as the fusion layer is constructed. Secondly, In order to improve the convergence speed and detection accuracy, the calculation method of intersection over union is improved, and the network is improved by using the Attention Mechanism. Finally, Particle Swarm Optimization algorithm (PSO) with adaptive parameters is introduced to optimize the hyperparameters of neural network, and a fluorescent magnetic particle inspection image acquisition platform is built for verification. The mean Average Precision (mAP) of the best model of EfficientNet-PSO on the validation set is 95.69%. F1 score is 0.94 and FLOPs is 1.86B. Compared with other five deep learning neural network models, this method effectively improves the defect detection efficiency and accuracy of flange plate and cylinder head, which can meet the defect detection requirements.

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“…With the popularity of deep learning, convolutional neural network models in the field of computer vision are constantly emerging. From initial 6-layer LeNet to 8-layer AlexNet and from 16-layer VGG16 to 152-layer ResNet, and even developed to DenseNet of thousands of layers [ 12 ]. While the performance of the deep learning network has improved, the structure of the network is getting complex, the number of parameters is getting larger and the speed of the network is getting slow, which makes it difficult to perform real-time detection on mobile and embedded devices in the industrial field.…”

Section: Related Workmentioning

confidence: 99%

Online Detection of Surface Defects Based on Improved YOLOV3

Chen

Fang

et al. 2022

Sensors

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Aiming at the problems of low efficiency and poor accuracy in the product surface defect detection. In this paper, an online surface defects detection method based on YOLOV3 is proposed. Firstly, using lightweight network MobileNetV2 to replace the original backbone as the feature extractor to improve network speed. Then, we propose an extended feature pyramid network (EFPN) to extend the detection layer for multi-size object detection and design a novel feature fusing module (FFM) embedded in the extend layer to super-resolve features and capture more regional details. In addition, we add an IoU loss function to solve the mismatch between classification and bounding box regression. The proposed method is used to train and test on the hot rolled steel open dataset NEU-DET, which contains six typical defects of a steel surface, namely rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. The experimental results show that our method achieves a satisfactory balance between performance and consumption and reaches 86.96%mAP with a speed of 80.96 FPS, which is more accurate and faster than many other algorithms and can realize real-time and high-precision inspection of product surface defects.

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Defect detection of printed circuit board based on lightweight deep convolution network

Cited by 32 publications

References 38 publications

Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm

Online Detection of Surface Defects Based on Improved YOLOV3

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