检索结果-内蒙古大学图书馆

2021 4th International Conference on E-Business, Information Management and Computer Science

作者： Yuehao Tang Chong Guo Changjiang Institute of Technology China Wuhan Business University China

ISBN: (纸本)9781450395687

Abstract: In order to improve the intellective level of water resources management, a real-time water level recognition method based on deep-learning algorithms and image-processing techniques is proposed in this paper. The recognition process is composed of four steps. Firstly, for the purpose of digit detection, YOLO-v3 model is deployed for extracting numbers from the water gauges. Then, the cropped number images are fed into the LSTM + CTC model as training samples so that digits can be recognized. In the third step, Hough transform are adopted to correct the tilt of water gauge in terms of the vertical edge feature. Morphological operation, associated with horizontal projection would position upper and lower edge of water gauge to recognize the scale lines correctly. Water level could be determined correspondingly. Model application shows that the recognition model has satisfying accuracy and efficiency, with potential being applied in practice.

关键词： Water Gauge image processing Number Recognition deep learning Water Level Number Detection

来源：评论

学校读者我要写书评

暂无评论

Weld Pool Boundary Detection Based on the U-Net Algorithm and Weld Seam Tracking in Plasma Arc Welding

引用

APPLIED SCIENCES-BASEL 2025年第4期15卷 1814-1814页

作者： Lu, Jidong Yamane, Satoshi Wang, Weixi Li, Ning Wang, Siqi Xia, Yuxiong Saitama Univ Grad Sch Sci & Engn Saitama 3388570 Japan Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

Plasma arc welding can achieve high-quality welding joints in high-strength manufacturing fields, such as aviation and automotive, and improve production efficiency. It is important to observe the weld pool state in real-time in robot automatic welding. However, the electrodes of the plasma welding torch cannot be observed from the outside. Teaching the weld line to torch in real-time to be observable to humans will be difficult. Also, it is difficult to process the image to obtain the position of the weld line in K-PAW. In this study, a camera was utilized to observe the weld pool. The authors estimate the weld line position in real time by image processing based on U-Net prediction. The U-Net model demonstrates sufficient prediction where the accuracy reached 99.5% for the training data and 96.5% for the test data recognition. Moreover, a control method utilized weld line position estimated from the boundary area to verify the effectiveness of this prediction model from 3 mm within the deviation of 1 mm, which is within the range of permissible welding errors. It could reduce image processing errors in the weld pool image and provide higher recognition accuracy than image processing. Combining vision sensing technologies and deep learning methods will provide new technologies to enable higher welding precision and improve welding quality. It could also accelerate the development of welding technology in the intelligent manufacturing field.

关键词： plasma arc welding U-Net image processing adaptive control deep learning

来源：评论

学校读者我要写书评

暂无评论

deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

引用

POWDER TECHNOLOGY 2024年 448卷

作者： Yang, Peng Guo, Qiankun Chen, Hongsheng Chongqing Univ Coll Mat Sci & Engn Dept Met Engn Chongqing 400044 Peoples R China

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, deepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.

关键词： Bubbling fluidized bed deep learning U-Net model Bubble recognition Bubble behavior

来源：评论

学校读者我要写书评

暂无评论

Syflo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines

引用

JOURNAL OF real-time image processing 2025年第1期22卷 1-15页

作者： Sankuri, Raja Sekhar Sristy, Nagesh Bhattu Karri, Sri Phani Krishna Natl Inst Technol Andhra Pradesh Dept Elect Engn Tadepalligudem India Natl Inst Technol Andhra Pradesh Dept Comp Sci & Engn Tadepalligudem India

Sustainable transmission of electrical energy to consumers across regions relies heavily on the integrity of power transmission lines and continuous monitoring of assets is crucial for maintaining system reliability. Unmanned aerial vehicles have revolutionized defect identification in real-time and accessibility, even in difficult-to-reach geographical landscapes, thereby improving image-based inspections. This work introduces semisupervised Yolo with focal loss function (SYFLo), a novel method that augments YOLO for real-time health monitoring of electric assets in power transmission lines. SYFLo integrates the focal loss function with semi-supervised learning to effectively address the lack of abundant labeled data, data imbalances and enhance detection accuracy. Additionally, it improves data generalizability across a wide range of images, ensuring robust performance despite varied image backgrounds. By leveraging YOLOv8, SYFLo significantly improves fault identification, achieving a detection accuracy of 96.5% and an FPS of 16.39. Experimental results demonstrate the impact of the proposed approach, highlighting its potential to enhance the reliability of power transmission line monitoring. These findings underscore the importance of integrating advanced deep learning techniques with innovative loss functions to address common challenges in real-time health monitoring systems.

关键词： deep learning Power grid inspection Defect detection Semi-supervised learning Focal loss function

来源：评论

学校读者我要写书评

暂无评论

Research on online measurement method of fineness modulus of manufactured sand based on deep learning

引用

CONSTRUCTION AND BUILDING MATERIALS 2024年 414卷

作者： Huang, Feizhi Yang, Jianhong Fang, Huaiying Pan, Weijun Wei, Yixing Huaqiao Univ Coll Mech Engn & Automat Xiamen 361021 Fujian Peoples R China

The fineness modulus(FM) represents the level of particle size of manufactured sand. real-time feedback of FM of manufactured sand is important for industrial sand production, but extracting the particle profile from densely stacked images is a great challenge. In this study, a deep learning and regression analysis -based online measurement method for FM of manufactured sand is proposed. Firstly, the real fineness modulus of the sand produced by the sand -making machine in real time was obtained by the vibration -screening method(VSM). Then, the particle size fraction of larger particles (0.6-4.75 mm) was obtained based on machine vision combined with a convolutional neural network and image processing. Secondly, a multiple linear regression model was developed for the percentage of particle size and FM. Finally, the percentage of particle size was substituted into the regression model as the independent variable to achieve a fast prediction of the unknown FM. The experimental results show that the maximum repeatability errors for FM of different manufactured sands are 0.09 and 0.13 respectively, and the maximum absolute errors of the FM prediction results are 0.18 and 0.17 respectively. The calculation efficiency and error level of this research method can meet the online testing at sand making sites.

关键词： Manufactured sand Fineness modulus deep learning Online measurement image processing

来源：评论

学校读者我要写书评

暂无评论

deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

引用

ASTRONOMY & ASTROPHYSICS 2025年 693卷

作者： Schirninger, C. Jarolim, R. Veronig, A. M. Kuckein, C. Karl Franzens Univ Graz Inst Phys Univ Pl 5 A-8010 Graz Austria Natl Ctr Atmospher Res High Altitude Observ 3080 Ctr Green Dr Boulder CO USA Karl Franzens Univ Graz Kanzelhohe Observ Solar & Environm Res Graz Austria Inst Astrofis Canarias IAC Via Lactea S-N E-38205 San Cristobal la Laguna Tenerife Spain Univ La Laguna Dept Astrofis E-38206 San Cristobal la Laguna Tenerife Spain Max Planck Inst Sonnensystemforsch Justus Von Liebig Weg 3 D-37077 Gottingen Germany

Context. Large aperture ground-based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, ground-based observations are inherently limited due to Earth's turbulent atmosphere, requiring image correction techniques. Aims. Recent post-image reconstruction techniques are based on using information from bursts of short-exposure images. Shortcomings of such approaches are the limited success, in case of stronger atmospheric seeing conditions, and computational demand. real-time post-image reconstruction is of high importance to enabling automatic processing pipelines and accelerating scientific research. In an attempt to overcome these limitations, we provide a deep learning approach to reconstruct an original image burst into a single high-resolution high-quality image in real time. Methods. We present a novel deep learning tool for image burst reconstruction based on image stacking methods. Here, an image burst of 100 short-exposure observations is reconstructed to obtain a single high-resolution image. Our approach builds on unpaired image-to-image translation. We trained our neural network with seeing degraded image bursts and used speckle reconstructed observations as a reference. With the unpaired image translation, we aim to achieve a better generalization and increased robustness in case of increased image degradations. Results. We demonstrate that our deep learning model has the ability to effectively reconstruct an image burst in real time with an average of 0.5 s of processing time while providing similar results to standard reconstruction methods. We evaluated the results on an independent test set consisting of high- and low-quality speckle reconstructions. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstruction methods show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image info

关键词： atmospheric effects techniques: image processing telescopes Sun: atmosphere Sun: photosphere

来源：评论

学校读者我要写书评

暂无评论

Identification of Criminal and Non-Criminal Face Using deep learning and image processing

Identification of Criminal and Non-Criminal Face Using Deep ...

引用

2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Raj Kumar, R. Shyam Sundar, A. Ruban Haris, B. Ragul, K. Cloudin, S. KCG College of Technology Chennai India

ISBN: (纸本)9781665439862

This paper executes a criminal face affirmation using deep learning Algorithm. The data is used to get ready and test using image processing count. The longing of this paper is to execute using a pretrained significant learning network called Convolution neural association. In this paper, we address a methodology for face acknowledgment using Densenet pre-trained of which is definite and less incidents count for picture face area. Now we use Densenet for classifiers faces. The accuracy of the face affirmation is high. The proposed structure can adequately see more than one face which is useful for quickly glancing through assumed individuals as the figuring time is low. In India, we have a system for seeing occupant called Aadhaar. Our technique works in future to perceive this as a citizenship data base we can isolate among resident and outsider and further inspect if the recognized individual is criminal. © 2021 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Efficient NPU-GPU scheduling for real-time deep learning inference on mobile devices

引用

JOURNAL OF real-time image processing 2025年第2期22卷 1-13页

作者： Yu, Chengwu Wang, Meng Chen, Shan Wang, Wanqi Fang, Weiwei Chen, Yanming Xiong, Neal N. Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Hubei Engn Res Ctr Intelligent Detect & Identifica Wuhan 430205 Hubei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Sul Ross State Univ Dept Comp Sci & Math Alpine TX 79830 USA

As the need for on-device artificial intelligence (AI) has increased in recent years, mobile devices tend to be equipped with multiple heterogeneous processors, including CPU, GPU, and Neural processing Unit (NPU). While NPUs can offer low-cost and real-time AI processing capabilities for deep Neural Network (DNN) inference, its limited resources often lead to a trade-off between performance and accuracy, potentially resulting in a non-trivial accuracy drop. To address this problem, we propose a new NPU-GPU Scheduling (NGS) framework for DNN-based video analytics. The main challenge lies in determining when and how to execute inference on the NPU/GPU to satisfy the performance objectives. To make more precise scheduling decisions, we first propose a new image complexity assessment model to replace the existing normalized edge density metric. Then, we formulate the scheduling problem with the objective of maximizing inference accuracy under the given latency constraint, and introduce an adaptive solution based on dynamic programming to determine which frames should be processed on the GPU and when to exit from inference for each of them. Extensive experiments conducted on a real mobile device show that our NGS framework substantially outperforms other solutions, and achieves a close-to-oracle performance.

关键词： Edge computing DNN inference Heterogeneous processors Task scheduling Dynamic Programming

来源：评论

学校读者我要写书评

暂无评论

SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Lee, Kyungsu Lee, Haeyun Hwang, Jae Youn Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Daegu Gyeongbuk Inst Sci & Technol DGIST Dept Elect Engn & Comp Sci Interdisciplinary Studies Artificial Intelligence Daegu 42988 South Korea

deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks, deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks. To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on anew indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems.

关键词： deep learning Semi-supervised learning Efficient learning Scene recognition Indoor localization

来源：评论

学校读者我要写书评

暂无评论

Seeing Through the Haze: A Comprehensive Review of Underwater image Enhancement Techniques

引用

IEEE ACCESS 2024年 12卷 145206-145233页

作者： Saad Saoud, Lyes Elmezain, Mahmoud Sultan, Atif Heshmat, Mohamed Seneviratne, Lakmal Hussain, Irfan Khalifa Univ Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi U Arab Emirates

Underwater imaging suffers from significant quality degradation due to light scattering and absorption by water molecules, leading to color cast and reduced visibility. This hinders the ability to analyze and interpret the underwater world. image dehazing techniques have emerged as a crucial component for underwater image enhancement (UIE). This review comprehensively examines both traditional methods, rooted in the physics of light transmission in water, and recent advances in learning-based approaches, particularly deep learning architectures like Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformers. We conduct a comparative analysis across various metrics, including visual quality, color fidelity, robustness to noise, and computational efficiency, to highlight the strengths and weaknesses of each approach. Furthermore, we address key challenges and future directions for traditional and learning-based methods, focusing on domain adaptation, real-time processing, and integrating physical priors into deep learning models. This review provides valuable insights and recommendations for researchers and practitioners in underwater image enhancement.

关键词： image color analysis image enhancement Sensitivity Reviews Colored noise image restoration Absorption Underwater navigation Underwater image enhancement traditional dehazing methods learning-based dehazing methods deep learning for underwater imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：