Development of hybrid optical sensor based on deep learning to detect and classify the micro-size defects in printed circuit board

Kaya, Gulhan Ustabas

doi:10.1016/j.measurement.2022.112247

Cited by 15 publications

(9 citation statements)

References 37 publications

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“…A growing number of neural network-based target detection algorithms have been developed and used in a variety of fields in recent decades, including image classification [17], industrial inspection [18], autonomous driving [19], intelligent visual charging technology [20][21][22], the fields of traffic signs and road object detection [23,24]. Modern target detection algorithms fall into two major categories.…”

Section: Related Workmentioning

confidence: 99%

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

Zhang,

Su,

Wang

et al. 2024

J Supercomput

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The presence of microparticle viruses significantly impacts the quality of silkworm seeds for domestic sericulture, making their exclusion from detection in silkworm seed production crucial. Traditional methods for detecting microparticle viruses in silkworms, such as manual microscopic observation, molecular biology, and immunological approaches, are cumbersome and unable to achieve intelligent, batch real-time detection. To address this challenge, we employ the YOLOv8 algorithm in this paper. Firstly, NAM attention is introduced in the original algorithm’s Backbone component, allowing the model to extract more generic feature information. Secondly, ODConv replaces Conv in the Head component of the original algorithm, enhancing the model’s ability to identify microparticle viruses. Finally, NWD-LOSS modifies the CIoU loss of the original algorithm to obtain a more accurate prediction box. Experimental results demonstrate that the NN-YOLOv8 model outperforms mainstream detection algorithms in detecting silkworm microparticle diseases. With an average detection time of 22.6 milliseconds per image, the model shows promising prospects for future applications. This model improvement enhances detection efficiency and reduces human resource costs, effectively realizing detection intelligence.

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Section: Related Workmentioning

confidence: 99%

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

Zhang,

Su,

Wang

et al. 2024

J Supercomput

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“…In their study published in [132], the authors proposed an approach to detect defects on large-sized PCBs and measure their copper thickness before the mass production process using a hybrid optical sensor HOS based on CNN. The method involves combining microscopic fringe projection profilometry (MFPP) with the lateral shearing digital holographic microscopy (LSDHM) for imaging and defect detection, utilizing an optical microscopic sensor with minimal components.…”

Section: Cnn-based Applicationsmentioning

confidence: 99%

“…20 Collect the temperature data along the optic fiber set and figure out the anomaly detected temperature at the early phase. 98% [136] 21 detection of the defects on large-sized PCBs and measure their copper thickness before the mass production process [132] of ambient temperature changes on the detection of sulfurized rust self-heating anomalies. This method did not consider diverse weather conditions such as strong winds, rainfall, and temperature variations resulting from seasonal changes or diurnal fluctuations.…”

Section: 9% [135]mentioning

confidence: 99%

Deep Learning for Optical Sensor Applications: An Insight Review

Al‐Ashwal¹,

Soufy²,

Hamza³

et al. 2023

Preprint

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Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

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“…Some examples presented in the literature address the detection of defects in printed circuit boards, size estimation of onions, and other objects of general character measured using different 3D reconstruction techniques. [5][6][7] Recently, a new deep-learning technique that takes advantage of the inherent symmetries in object detection was developed in microscopy. 8 This method allows it to be trained on extremely small datasets, even as little as a single image of the object, without requiring any ground truth.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning enabled single-shot 3D feature detection in fringe projection profilometry

Peña

Benjumea

Vargas

et al. 2023

Dimensional Optical Metrology and Inspection for Practical Applications XII

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Reliably detecting or tracking 3D features is challenging. It often requires preprocessing and filtering stages, along with fine-tuned heuristics for reliable detection. Alternatively, artificial intelligence-based strategies have recently been proposed; however, these typically require many manually labeled images for training. We introduce a method for 3D feature detection by using a convolutional neural network and a single 3D image obtained by fringe projection profilometry. We cast the problem of 3D feature detection as an unsupervised detection problem. Hence, the goal is to use a neural network that learns to detect specific features in 3D images using a single unlabeled image. Therefore, we implemented a deep-learning method that exploits inherent symmetries to detect objects with few training data and without ground truth. Subsequently, using a pyramid methodology of rescaling each image to be processed, we achieved feature detections of different sizes. Finally, we unified the detections using a Non-Maximum Suppression algorithm. Preliminary results show that the method provides reliable detection under different scenarios with a more flexible training procedure than other competing methods.

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Development of hybrid optical sensor based on deep learning to detect and classify the micro-size defects in printed circuit board

Cited by 15 publications

References 37 publications

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

Deep Learning for Optical Sensor Applications: An Insight Review

Deep-learning enabled single-shot 3D feature detection in fringe projection profilometry

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