HRIPCB: a challenging dataset for PCB defects detection and classification

Defect detection is an essential requirement for quality control in the production of printed circuit boards (PCBs) manufacturing. The traditional defect detection methods have various drawbacks, such as strongly depending on a carefully designed template, highly computational cost, and noise-susceptibility, which pose a significant challenge in a production environment. In this paper, a deep learning-based image detection method for PCB defect detection is proposed. This method builds a new network based on Faster RCNN. We use a ResNet50 with Feature Pyramid Networks as the backbone for feature extraction, to better detect small defects on the PCB. Secondly, we use GARPN to predict more accurate anchors and merge the residual units of ShuffleNetV2. The experimental results show that this method is more suitable for use in production than other PCB defect detection methods. We have also tested in other PCB defects dataset, and experiments have shown that this method is equally valid.

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“…After using ShuffleNetV2, the network's detection efficiency is improved, which can meet the needs of real-time detection. [28].…”

Section: D)mentioning

confidence: 99%

Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

Wang

2020

IEEE Access

161

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“…Such a late adoption of deep learning for PCB inspection was due to various limiting characteristics in this field; for example, database acquisition is difficult and relevant companies are often reluctant to release data. Thus, research papers are often written by companies with PCB production lines [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

“…Huang et al [ 17 ] have suggested the use of HRIPCB, a PCB dataset without assembled components, to address the dearth of available data. Indeed, the disclosure of large volumes of data has greatly contributed to the field of study by providing public data that has become a new standard because of an increased accessibility that it provides.…”

Section: Related Workmentioning

confidence: 99%

Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Gang

Fabrice

Chung

et al. 2021

Sensors

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As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.

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“…The images in the PCB DSLR dataset composed with information about bounding box and segmentation of Integrated chips [8]. Another, widely used dataset is synthesized PCB dataset which is constructed by Weibo Huang, Peng Wei [9]. The synthesized PCB dataset used for detecting, classification and registration task and the dataset composed with 1386 images along with six types of defects.…”

Section: A Input Image Collectionmentioning

confidence: 99%

Defect Detection in Printed Board Circuit using Image Processing

Munisankar*¹,

Nagarajan²,

Rao³

2019

IJITEE

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A printed circuit board without connecting with any components called as a bare PCB. Consider a PCB as a basic part which has been settled with more electronic units. In order to display the manufacturing process, the drawbacks have been taken by PCB individually. The reflection of this separation process impacts the performance of the circuits. Also, we have examined about classification methodologies as well as referential based PCB detection. From the input images, the needed and related information has been pulled out using image processing methodologies by the referential based PCB detection. Comparing with the un-defected PCB images, this was used to find out the defects. To meet the goal of the PCB defect detection, several feature extraction and pre-processing methods are derived in this article. The PCB defects have been classified by those features using the machine learning algorithms. Moreover, several types of machine learning algorithms are derived in this article. This paper helps the researchers for achieving a better solution for image processing and machine learning-based printed circuit board the defect classification

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HRIPCB: a challenging dataset for PCB defects detection and classification

Cited by 57 publications

References 17 publications

Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Defect Detection in Printed Board Circuit using Image Processing

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