Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee ...
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Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee precise component placement, potentially leading to product failures, user hazards, and substantial financial and reputational losses. This research presents a workflow to automate an inspection system that integrates computer vision, machine learning, imageprocessing, and control systems to address these challenges. The proposed system employs a microcontroller and stepper motors to control a highly calibrated camera, enabling precise and efficient product inspection. At its core, the system utilizes the YOLOv5 model for object detection, specifically identifying hole marks and holes on products pre-assembly. This deep learning model was chosen for its real-time detection capabilities and high accuracy, achieving a mean Average Precision (mAP) of 0.95, which surpasses many current industry standards. Following object detection, advanced imageprocessing techniques are applied to determine the precise position of detected features. Our approach achieves a notable error rate of 0.2 %, offering improvements over traditional inspection methods. Our system offers the potential to reduce inspection processing time and improve fault identification accuracy in real-time applications. Our research contributes to the field of industrial automation by introducing a seamless integration of state-of-the-art computer vision techniques with practical control systems. The system's modular design allows for easy adaptation to various manufacturing environments, benefiting industries with complex assembly processes, such as electronics, automotive manufacturing, etc. While the current implementation focuses on hole detection, future work will explore expanding the system's capabilities to identify a broader range of defects and adapt to different product types. This re
With the rapid advancement in wafer packaging technology, especially the surging demand for chips, enhancing product quality and process efficiency has become increasingly crucial. This article delves into the automat...
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With the rapid advancement in wafer packaging technology, especially the surging demand for chips, enhancing product quality and process efficiency has become increasingly crucial. This article delves into the automatic detection of pins on Ball Grid Array (BGA) within wafer packaging processes. This system is engineered with a flexible software and hardware architecture to address evolving industrial requirements, facilitating swift adaptation to new processing standards and technological demands. By utilizing Programmable Logic Controller (PLC) to control a three-axis gantry slide combined with industrial camera imaging technology, this system achieves high efficiency and precise positioning, thereby delivering high-quality image. This article utilizes YOLOv10 imageprocessing technology and machine learning algorithms to effectively achieve accurate identification and classification of BGA defects. The YOLOv10 is chosen for its outstanding recognition capabilities and swift processing speed, enabling the rapid and accurate identification of minor defects, such as bent pins, missing pins, and solder ball defects. Through large image analysis, this system has been proven to enhance detection accuracy and reduce errors of manual detection. This article primarily addresses issues in semiconductor manufacturing processes and improves the product yield rate in current production lines. By effectively integrating AI-based detection technology into semiconductor manufacturing, it replaces labor-intensive tasks, enhancing efficiency and precision.
Artificial neural networks have been one of the science's most influential and essential branches in the past decades. Neural networks have found applications in various fields including medical and pharmaceutical...
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Artificial neural networks have been one of the science's most influential and essential branches in the past decades. Neural networks have found applications in various fields including medical and pharmaceutical services, voice and speech recognition, computer vision, natural language processing, and video and imageprocessing. Neural networks have many layers and consume much energy. Approximate computing is a promising way to reduce energy consumption in applications that can tolerate a degree of accuracy reduction. This paper proposes an effective method to prevent accuracy reduction after using approximate computing methods in the CNNs. The method exploits the k-means clustering algorithm to label pixels in the first convolutional layer. Then, using one of the existing pruning methods, different pruning amounts have been applied to all layers. The experimental results on three CNNs and four different datasets show that the accuracy of the proposed method has significantly improved (by 17%) compared to the baseline network.
machine learning(ML)is increasingly applied for medical imageprocessing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws...
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machine learning(ML)is increasingly applied for medical imageprocessing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
machinevision measurement is desirable to permit real-time non-contact measuring and positioning for hot forgings, among which edge extraction is a most essential issue to extract the contour and effective area. Howe...
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machinevision measurement is desirable to permit real-time non-contact measuring and positioning for hot forgings, among which edge extraction is a most essential issue to extract the contour and effective area. However, conventional edge detection methods are prone to get unsatisfactory edging extraction results, thus have poor effectiveness, and are not suitable for hot forging images. In this paper, an efficient and robust edge extraction approach for passive visionimages of hot forgings is proposed. Grayscale images of hot forgings converted into discrete gray surface, the approach is based on the geometric properties and the continuity of the equivalent discrete grayscale surface. The presented algorithm detects three types of edges by various continuity criterions, which are corresponded to the geometric properties and vary with the primary and secondary edges. The geometric properties dependent nature of the algorithm ensures the primary and the secondary edges of the forges are identified in the different environmental conditions and for forging parts with various heat radiation intensities. Moreover, an edge thinning and connection approach is presented by defining the edging direction, which can be used to improve the qualities of types of edges. Finally, experimentations for images of various sorts of hot forgings are carried out to extract three types of edges;the relevant experimental results and validation indicators show that the proposed method takes better performance as 17.4453 in PSNR and 0.1146 in entropy for G0 edge for a typical forging image while 0.0342 for G2 edge compared with existing methods. The results demonstrate that the proposed approach is validated to have satisfactory performance, as well as efficacy and robustness.
As a ubiquitous manipulation tool, optical tweezers are widely used in biochemistry and applied physics, so that a wide range of microscopic and nanoscopic particles could be investigated. In recent years, digital ima...
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As a ubiquitous manipulation tool, optical tweezers are widely used in biochemistry and applied physics, so that a wide range of microscopic and nanoscopic particles could be investigated. In recent years, digital imageprocessing techniques for improving target particle observation have diversified, leading to the development of numerous automatic tasks. The technique was developed in response to the need for multi-particle manipulation and feature detection. Here we describe how digital imageprocessing can be used to enhance the capabilities of optical manipulation. In particular, cutting-edge imageprocessing techniques that rely on artificial intelligence development are making optical trapping more widely accessible and enabling automatic manipulation of microscopic and nanoscopic particles.
In the machinevision-based online monitoring of the flotation process, froth images acquired in real-time are subject to color distortion and excessive bright spots caused by inconsistent illumination, which hinders ...
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In the machinevision-based online monitoring of the flotation process, froth images acquired in real-time are subject to color distortion and excessive bright spots caused by inconsistent illumination, which hinders the effectiveness of image analysis and further online measurement for operating performance indicators. Current imageprocessing methods struggle to correct color distortion and remove excess bright spots in froth images simultaneously. Therefore, in this article, an illumination domain signal-guided unsupervised generative adversarial network (IDS-GUGAN) is proposed for illumination consistency processing of flotation froth images. First, considering the varying effects of inconsistent illumination on froth images, the illumination domain signal-guided image generation (IDS-GIG) mechanism based on the theory of unsupervised disentangled representation learning is designed to achieve adaptive correction of froth images with varying degrees of distortion. Moreover, a novel lightweight double-closed-loop network architecture is introduced to support unsupervised learning utilizing unpaired froth images and improve computational efficiency, which makes the proposed approach highly suitable for industrial applications. Comprehensive experiments on a real tungsten cleaner flotation process dataset and two public benchmark datasets related to image illumination processing tasks consistently endorse the superiority of IDS-GUGAN.
imageprocessing and artificial intelligence techniques represent new and effective tools for supporting archaeological research to bring ancient finds to light. They can help archaeologists to discover remains that a...
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imageprocessing and artificial intelligence techniques represent new and effective tools for supporting archaeological research to bring ancient finds to light. They can help archaeologists to discover remains that are difficult to identify using traditional approaches. The design and development of such applications, which aim at processing large amounts of data to cover extended areas, requires the use of Cloud paradigms for exploiting Cloud elasticity and scaling with the problem size. This paper presents an original methodology that integrates deep learning, computer vision, and optimization models to identify archaeological remains from aerial images. Results demonstrate how the proposed approach can search for the remains of Centuriation, which is an ancient Roman system for dividing the land over a large area, and evaluate the scalability of a map-reduce implementation in the Cloud.
The high speed, wide bandwidth, and parallel processing capabilities of a diffractive optical neural network (DONN) stimulate its applications in computer vision for image recognition and information processing tasks....
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In this study, an ad-hoc imageprocessing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian ...
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In this study, an ad-hoc imageprocessing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machinevision, particularly for food product quality assessment and real-time inspection.
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