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A Review in the Use of Artificial Intelligence in Textile Industry 1st

A Review in the Use of Artificial Intelligence in Textile In...

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1st International conference on Innovation in Engineering (ICIE)

作者： Pereira, Filipe Carvalho, vitor Vasconcelos, Rosa Soares, Filomena Univ Minho Algoritmi R&D Ctr Guimaraes Portugal IPCA Sch Technol 2Ai Lab Barcelos Portugal Univ Minho 2C2T R&D Ctr Guimaraes Portugal

ISBN: (纸本)9783030791681;9783030791674

This paper presents an analysis of the state of the art of artificial intelligence applications in the textile industry. A review of the existing literature was performed. This article presents three methods of analyzing textile yarn. Some techniques, used in textile fabric inspection, are presented throughout this paper, as well as the use of artificial intelligence on improving the performance of productive systems using neural networks and artificial vision. The preliminary results demonstrate that the techniques covered are an asset in obtaining defects in textile fabrics at the industrial level. Taking into account the various methods of inspection and analysis of textile yarn, all present pros and cons in applicability in the textile area. In terms of advantages, all allow a better analysis of the textile yarn and defect detection with high quality, but with applicability in more complex systems. As a disadvantage, they present the fact that they do not have an already standardized algorithm that can be used, which makes its use more complex. Some possible future applications are also described.

关键词： Artificial intelligence (AI) Textile industry Industry 4.0 Yarn evaluation machine learning image processing

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applications of Superpixels on Phase Contrast Microscopy

Applications of Superpixels on Phase Contrast Microscopy

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First International conference on Transdisciplinary AI (TransAI)

作者： vincent On Atena Zahedi Rattapol Phandthong Department of Computer and Electrical Engineering and Computer Science California State University Bakersfield Bakersfield CA USA Stem Cell Research Center University of California Irvine Irvine CA USA Department of Molecular Cell and Systems Biology University of California Riverside Riverside CA USA

Cellular microscopy is enhanced by computational paradigms such as image processing, computer vision, and machine learning. image segmentation is vital for quantifying cell images, enabling tracking and subsequent applications. Superpixel algorithms offer a promising solution to challenges in phase contrast image segmentation, as they group pixels with similar characteristics into meaningful regions. By preserving essential structural information and reducing complexity, superpixels improve accuracy in cell-related measurements and facilitate the study of dynamic cellular processes. This paper presents a comparison of popular superpixel algorithms applied to cellular microscopy, highlighting their contributions to image analysis and interpretation in cell biology research.

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Transparent machine vision Techniques For Medicinal Plant Species Identification

Transparent Machine Vision Techniques For Medicinal Plant Sp...

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image processing and Capsule Networks (ICIPCN), International conference on

作者： Christine Kikome Geoffrey Okumu Emmanuel Wagisha Daudi Jjingo John Kizito Ggaliwango Marvin Computer Science Department Makerere University Kampala Uganda

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Medicinal plants have long been the foundation of the medical system and a source of health and healing, but many people nowadays are unaware of these priceless natural resources or the range of possible applications they provide. Traditional medical professionals known as herbalists mostly depend on their understanding of therapeutic herbs to cure a wide range of illnesses. Unfortunately, identifying medicinal plants by hand from their leaves takes a lot of time, labour, and specialised knowledge. The purpose of this project was to create and construct a web application for the identification of plant species, namely medicinal plants that are present across Uganda. On a publicly available dataset, many machine learning and deep learning models, including Convolutional Neural Networks (CNN), VGG19 and 16, Resnet50, vision Transformer (viT), and EfficientNet B0, were trained. Many features in the dataset were extracted from each leaf such as its length, width, texture, area, and color. For the ten classes of medicinal plant species identified in the globe, the model’s accuracy rates were 99%, 95.70%, $\mathbf{9 2. 5 8 \%}, \mathbf{9 7. 9 0 \%}, \mathbf{9 1. 2 0 \%}$, and $\mathbf{3 5. 6 9 \%}$, respectively. In order to improve transparency and visual explanations for its picture classifications, this model makes use of Grad-CAM and SHAP, which are explainable AI (XAI) approaches. It is anticipated that a web-based system for the automatic identification of medicinal plants will contribute significantly to the production of pharmaceutical drugs, help members of the community increase their knowledge of medicinal plants, and assist taxonomists in creating more effective methods for identifying species.

关键词： Medicinal plants Deep learning Training visualization Accuracy Transformers Feature extraction

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Crop Disease and Pest Detection using Convolutional Neural Networks (CNN)

Crop Disease and Pest Detection using Convolutional Neural N...

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image processing and Capsule Networks (ICIPCN), International conference on

作者： S Kalaimanivel K France Department of Computer Applications 22295014 Hindustan institute of technology and science Chennai India Department of Computer Applications Hindustan institute of technology and science Chennai India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people’s lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. Artificial Intelligence (AI) and machine Learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. This study suggests a novel technique for automated crop disease identification by utilizing Convolutional Neural Networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. The Plant village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. The proposed method has achieved an accuracy of about 99.6%.

关键词： Training Accuracy Prevention and mitigation Crops Production Soil Loss measurement

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machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

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2023 International conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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Design and Development of Industrial vision Sensor (IviS) for Next Generation Industrial applications

Design and Development of Industrial Vision Sensor (IVIS) fo...

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IEEE India conference (INDICON)

作者： Jerry Daniel J Lijo Thomas Lajitha C S Jacob T Mathew Jithin S Anju Mohan Kichu S Kumar Control & Instrumentation Group Centre for Development of Advanced Computing (C-DAC) Thiruvananthapuram Kerala India

ISBN: (纸本)9781665452724

Industrial automation is undergoing a tremendous change due to the proliferation of the concepts, the Internet of Things (IoT), Cyber-Physical Systems (CPS) and tactile internet, which enables the interconnections of factory floor devices and enterprise network on a wider and fine-grained scale. vision Sensor deployments are getting great momentum in factories, as it improves the quality and productivity of the systems being inspected. Smart vision Sensors[1] removes the need of the additional infrastructures for running the image processing algorithms and vision applications, by directly running the vision logic on the device and control/monitor the various parameters on the field based on the image processing outputs. Industrial vision sensor (IviS) is an industrial smart camera, which has a CMOS image sensor[2] and a powerful on-board processing system capable of supporting machine vision applications, for improving the product and process qualities and thereby improve the yield and profit. IviS is capable of extracting application-specific information from the captured images and make decisions based on the image processing algorithms implemented on the system, to realize stand-alone intelligent and decision-making automation system. In this paper we present the design and development of IviS, its application domains and preliminary test results.

关键词： Tactile Internet Productivity Automation machine vision Decision making Smart cameras vision sensors

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Accelerated identification method of industrial instrument based on yolov5

Accelerated identification method of industrial instrument b...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Shimao Yu Haiyang Zheng Chunhe Song Pengpei Gao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665464697

There are many problems in industrial instrument detection, such as complex background and visual Angle, etc. Traditional machine learning methods are difficult to achieve high-precision object detection and real-time monitoring applications. This paper proposes an accelerated identification method of industrial instruments based on YOLOv5. First, sample data are collected on site for image annotation, and YOLOv5 network structure is designed for transfer learning training, so as to realize image recognition of four types of industrial instruments. After that, the model is accelerated based on TensorRT on Jetson TX2 platform. The speed of instrument online detection reached 30 fps and mAP0.5 is 0.989.

关键词： Training visualization Computer vision image recognition Instruments Computational modeling Transfer learning

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Underwater Object Detection Enhancement via Channel Stabilization

Underwater Object Detection Enhancement via Channel Stabiliz...

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2022 International conference on Digital image Computing: Techniques and applications, DICTA 2022

作者： Ali, Muhammad Khan, Salman Department of Machine Learning Mohamed bin Zayed University of AI Abu Dhabi UAE Department of Computer Vision Mohamed bin Zayed University of AI Abu Dhabi UAE

ISBN: (纸本)9781665456425

The complex marine environment exacerbates the challenges of object Abstract-The complex marine environment exacerbates the challenges of object detection manifold. With the advent of the modern era, marine trash presents a danger to the aquatic ecosystem, and it has always been challenging to address this issue with complete grip. Therefore, there is a significant need to precisely detect marine deposits and locate them accurately in challenging aquatic surroundings. To ensure the safety of the marine environment caused by waste, the deployment of underwater object detection is a crucial tool to mitigate the harm of such waste. Our work explains the image enhancement strategies used and experiments exploring the best detection obtained after applying these methods. Specifically, we evaluate Detectron 2's backbones performance using different base models and configurations for the underwater detection task. We first propose a channel stabilization technique on top of a simplified image enhancement model to help reduce haze and colour cast in training images. The proposed procedure shows improved results on multi-scale size objects present in the data set. After processing the images, we explore various backbones in Detectron2 to give the best detection accuracy for these images. In addition, we use a sharpening filter with augmentation techniques. This highlights the profile of the object which helps us recognize it easily. We demonstrate our results by verifying these on TrashCan Data set, both instance and material version. We then explore the best-performing backbone method for this setting. We apply our channel stabilization and augmentation methods to the best-performing technique. We also compare our detection results from Detectron2 using the best backbones with those from Deformable Transformer. The detection result for small size objects in the Instance-version of TRASHCAN 1.0 gives us a 9.53box we get the absolute gain of 7to the baseline. © 2022 IEEE.

关键词： image enhancement

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Perceptual Learning Technology Based on Computer vision and machine Learning Algorithms 14

Perceptual Learning Technology Based on Computer Vision and ...

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14th IEEE International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Wang, Tianjian School of Computer Science and Technology Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798331531935

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security. © 2025 IEEE.

关键词： image segmentation

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Deep Regression Tracking with Graph Attention

Deep Regression Tracking with Graph Attention

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Xie Chen Dawei Zhang Zhonglong Zheng Yiran He Zhejiang Normal University JinHua China

ISBN: (纸本)9781665464697

Deep regression tracking is one of the fastest tracking algorithms due to its simplicity of framework, and therefore it is suitable for practical applications. However, most regression-based trackers do not make good balance between tracking speed and accuracy. Besides, the features of two adjacent frames are simply concatenated together without feature fusion operations, which limits the tracking accuracy. To address the above issues, we present a deep regression network with graph attention for visual tracking, which makes a trade-off between tracking speed and accuracy. To be specific, we introduce the graph attention network into the deep regression tracking framework to establish part-to-part correspondence between the two adjacent frames, and propagate the target information from the previous feature to the current feature. Experiments on challenging benchmarks including GOT-10k, OTB-100 demonstrate the competitive performance of the proposed algorithm in comparison with the state-of-the-art trackers.

关键词： visualization Computer vision Target tracking image processing Aggregates machine learning Benchmark testing

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