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Intelligent representation method of image flatness for cold rolled strip

Journal of Iron and Steel Research International 2024年第5期31卷 1177-1195页

作者： Yang-huan Xu Dong-cheng Wang Hong-min Liu Bo-wei Duan National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan UniversityQinhuangdao066004HebeiChina National Key Laboratory of Metal Forming Technology and Heavy Equipment China National Heavy Machinery Research Institute Co.Ltd.Xi’an710018ShaanxiChina State Key Laboratory of Metastable Materials Science and Technology Yanshan UniversityQinhuangdao066004HebeiChina

real flatness images are the bases for flatness detection based on machine vision of cold rolled *** characteristics of a real flatness image are analyzed,and a lightweight strip location detection(SLD)model with deep semantic segmentation networks is *** interference areas in the real flatness image can be eliminated by the SLD model,and valid information can be *** this basis,the concept of image flatness is proposed for the first *** image flatness representation(IFAR)model is established on the basis of an autoencoder with a new *** optimal structure of the bottleneck layer is 16×16×4,and the IFAR model exhibits a good representation ***,interpretability analysis of the representation factors is carried out,and the difference and physical meaning of the representation factors for image flatness with different categories are *** flatness with new defect morphologies(bilateral quarter waves and large middle waves)that are not present in the original dataset are generated by modifying the representation factors of the no wave ***,the SLD and IFAR models are used to detect and represent all the real flatness images on the test *** average processing time for a single image is 11.42 ms,which is suitable for industrial *** research results provide effective methods and ideas for intelligent flatness detection technology based on machine vision.

关键词： Cold rolled strip image flatness Location detection Representation learning Bottleneck layer

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learning Satellite image Recovery Through Turbulence

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JOURNAL OF THE ASTRONAUTICAL SCIENCES 2024年第5期71卷 1-20页

作者： Chang, Kimmy Fletcher, Justin Space Syst Command A&AS Kihei HI 96753 USA

This paper presents a study of deep learning approaches to image recovery using spatially-extended sequential observations of near-Earth satellites. image recovery is often a prerequisite for use of ground-based extended imagery in space domain awareness (SDA) due to aberrations induced by atmospheric turbulence along the path from satellite to sensor. Traditional deconvolution-based image recovery methods are sensitive to factors such as observation sequence length and estimates of the point spread function (PSF), which has motivated recent interest in autoencoders and other learned approaches. However, no previous study has applied general state-of-the-art image restoration models to the space domain data. In this work, we evaluate the effectiveness of recent deep learning methods, specifically Generative Adversarial Networks (GANs) and Vision Transformers, for image restoration of satellites. We analyze the trade-offs between restoration quality, time, and computational complexity of each method. We experimentally demonstrate that deep learning models provide high-quality image restoration with less data than traditional deconvolution methods. We further optimize the most successful state-of-the-art model and demonstrate its efficacy in image restoration at a previously unseen degradation level (SNIIRS = 2.5). Our deep learning models are trained on simulated data from the SILO dataset and require no training on real data, yet they restore the most severely degraded real satellite imagery with state-of-the-art performance of 27.0 dB PSNR and 0.95 SSIM on the SILO dataset, as well as better visual results on the real satellite images.

关键词： image restoration Superresolution Satellites Turbulence Ground-based imagery

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Illuminate the night: lightweight fusion and enhancement model for extreme low-light burst images

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JOURNAL OF real-time image processing 2024年第6期21卷 185页

作者： Avsar, Hasan Sarigul, Mehmet Karacan, Levent Iskenderun Tech Univ Comp Engn Dept Hatay Turkiye Cukurova Univ Dept Comp Engn Adana Turkiye Gaziantep Univ Dept Comp Engn Gaziantep Turkiye

Taking photographs under low ambient light can be challenging due to the inability of camera sensors to gather sufficient light, resulting in dark images with increased noise and reduced image quality. Standard photography techniques and traditional enhancement methods often fail to provide satisfactory solutions for images captured under extremely low ambient light conditions. To address this problem, data-driven methods have been proposed to model complex non-linear relationships between extremely dark and long-exposure images. Recently, burst photography has become interested in improving single-image low-light image enhancement to provide more information about the scene. In this study, we propose a novel unified fusion and enhancement model inspired by recent advancements in learning-based burst image processing methods. Our model processes a burst set of raw input images across multiple scales to fuse complementary information and predict possible enhancements over the fused information, thereby producing images with longer exposure. Additionally, we introduce a new data augmentation technique, the amplification ratio scaling multiplier, for training to further improve generalization. Experimental results demonstrate that our model achieves state-of-the-art performance in the perceptual metric LPIPS while maintaining highly competitive distortion metrics PSNR and SSIM compared to existing low-light burst image enhancement techniques.

关键词： Extremely low light Extremely dark image enhancement deep learning Burst photography

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deep learning-based methods for detecting defects in cast iron parts and surfaces

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IET image processing 2024年第1期18卷 47-58页

作者： Wang, Pengyu Jing, Peng Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan Peoples R China Sanlian Pump Ind Co Ltd Maanshan Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China

The large size tolerance and positional differences of burrs in cast iron blanks make it easy for traditional teaching polishing paths to cause overcutting or undercutting. Rapid and accurate identification of burrs and real-time correction of polishing trajectories are key technical issues for achieving high-precision polishing. Here, a deep learning-based method for defect detection in cast iron parts and surfaces is proposed. Firstly, a self-made dataset of cast iron parts and surface defects is created and annotated, and a variety of data augmentation methods are used to expand the number of samples in the original dataset, alleviating the problem of small sample size. Then, the coordinate attention mechanism is introduced into the backbone network to allocate more attention to the defect target. Finally, the bidirectional weighted feature pyramid network (BiFPN) is used in the feature fusion network to replace the original path aggregation network, improving the model's ability to fuse features of different sizes. Experimental results show that compared with the original model, the mean average precision (mAP) is increased by 3.1%, and the average precision (AP) in defect classification is increased by 7.6%, with an FPS of 112, achieving accurate and efficient real-time detection of cast iron parts and surface defects. First, this article used multiple data augmentation methods to alleviate the problem of small sample size in casting datasets. Second, attention mechanism was introduced. Finally, a novel feature fusion layer structure was adopted to improve the original network model. The experiment shows that compared with the original network model, the improved model proposed here has increased the accuracy of casting surface defect recognition category by 7.6%.image

关键词： convolutional neural nets deburring image fusion learning (artificial intelligence)

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Identification and characteristic analysis of internal defects in rock-filled concrete based on deep learning method

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MATERIALS TODAY COMMUNICATIONS 2024年 41卷

作者： Ren, Tao Zhang, Yufan Yang, Tao Chen, Qiaolin Yu, Lei Wang, Guoji Song, Meizhou Guizhou Univ Coll Civil Engn Guiyang 550025 Peoples R China Guiyang Shougang Real Estate Dev Co Guiyang 550025 Peoples R China

The distribution characteristics and geometric morphology characteristics of defects within RFC are important factors affecting the strength properties and rupture morphology of RFC. However, the excessive size of commonly used aggregates for RFC leads to difficulties in conducting in-depth experimental studies indoors. Based on the improved U-Net and image processing technology, this research establishes an integrated model for the identification, classification, and extraction of defects inside the RFC, quantitatively counts and analyzes the acquired defect distribution characteristics and geometrical morphology characteristics, and establishes a defect characteristic distribution function that can be used for the numerical reconstruction of defects. In order to realize the acceleration of U-Net training using training weights, use VGG-16 with the fully connected layer removed instead of the Encoder part of the U-Net. The integrated model in this research can realize automatic identification, classification, and extraction of multiple types of defects at the same time, and the established distribution function of defect characteristics provides a data basis and new ideas for the establishment of RFC three-dimensional numerical models containing real defects.

关键词： deep learning improved U -Net image processing technology defect characteristic function rock-filled concrete

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Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer

Edge deployed satellite image classification with TinEViT a ...

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Conference on real-time image processing and deep learning

作者： Halford, Gavin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673878;9781510673861

Lower resolutions and a lack of distinguishing features in large satellite imagery datasets make identification tasks challenging for traditional image classification models. Vision Transformers (ViT) address these issues by creating deeper spatial relationships between image features. Self attention mechanisms are applied to better understand not only what features correspond to which classification profile, but how the features correspond to each other within each separate category. These models, integral to computer vision machine learning systems, depend on extensive datasets and rigorous training to develop highly accurate yet computationally demanding systems. Deploying such models in the field can present significant challenges on resource constrained devices. This paper introduces a novel approach to address these constraints by optimizing an efficient Vision Transformer (TinEVit) for real-time satellite image classification that is compatible with ST Microelectronics AI integration tool, X-Cube-AI.

关键词： ViT STM-32 X-Cube-AI Satellite image Classification Efficient AI EuroSAT

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Radar Signal processing and Its Impact on deep learning-Driven Human Activity Recognition

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SENSORS 2025年第3期25卷 724-724页

作者： Ayaz, Fahad Alhumaily, Basim Hussain, Sajjad Imran, Muhammad Ali Arshad, Kamran Assaleh, Khaled Zoha, Ahmed Univ Glasgow James Watt Sch Engn Glasgow City G12 8QQ England Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn POB 346 Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr POB 346 Ajman 346 U Arab Emirates

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner-Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing.

关键词： human activity classification radar domain representations deep learning computational cost transfer learning

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On the Design of a Light-Weight deep learning Framework for Embedding in 5G Software Modem

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IEEE ACCESS 2024年 12卷 151008-151018页

作者： Min, Woonggyu Kang, Seungwoo Kim, Juyeop Jo, Ohyun Chungbuk Natl Univ Dept Comp Sci Cheongju 28644 South Korea Sookmyung Womens Univ Dept Elect Engn Seoul 04310 South Korea

In this paper, we present a light-weight deep learning framework specifically designed and implemented for embedding in 5G software modems. The framework is developed completely using the C language to operate in real time by being mounted on a software modem. The framework incorporates an imagification process proposed by the authors which can enhance efficient reference signal classification in constrained environments. Imagification is the proposed technique that converts radio signal data, which is sequence data, into image form, processing the data into a structure similar to the RGB color model used in traditional images. This enables the application of convolutional operations to reduce complexity and training time. The performance of the framework is validated using a spec-compliant 5G software modem testbed developed by the authors, achieving up to 99.7% accuracy even at a relatively low SNR of -2.74 dB. These results demonstrate the feasibility of integrating the deep learning framework into a practical 5G software modem. Additionally, we perform hyperparameter optimization to identify the most suitable learning structure for the system. The developed source code is available at Github for public use.

关键词： 5G deep learning deep learning software modem software modem wireless communication wireless communication wireless communication

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An Empirical Study on Teaching Management and Quality Feedback Based on Human Behavior Detection

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2024年第1期36卷 1页

作者： Zhang, Qian Osman, Siti Zuraidah Md Li, Hongming Lin, Xiao Univ Sains Malaysia Sch Educ Studies Minden Malaysia Univ Florida Coll Educ Gainesville FL USA Shandong Univ Arts Sch Int Art Exchange Jinan Peoples R China

In the current education field, the assessment of teaching management quality mostly relies on subjective judgment and static data, and lacks a real-time and dynamic feedback mechanism. In this study, we propose a deep learning-based human behavior analysis method, which aims to assess teaching management quality in real time by analyzing the behaviors of teachers and students in the classroom. First, in order to detect individual students in the video stream, an augmented detection framework based on YOLO v5s is introduced to process and analyze human actions and interaction patterns in the video data. Immediately after that, we design a channel residual decoupled convolutional neural network to recognize the different states of students. Teaching management quality is assessed by detecting students' classroom attention scores. By conducting experiments in different disciplines and teaching management environments to collect and train the model, the results show that the method can effectively improve the objectivity and accuracy of teaching management quality assessment.

关键词： deep learning Object Detection Behavior Recognition Teaching Management Quality image processing

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deep U-NET Based Heating Film Defect Inspection System

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2024年第4期25卷 759-771页

作者： Hwang, J. W. Park, H. J. Yi, H. Kyungpook Natl Univ Depart Mech Engn 80Daehak Ro Daegu 41566 South Korea

This study introduces a real-time, high-resolution image inspection system that utilizes multiple cameras and deep learning algorithms for the real-time detection of pinholes and scratches on large-area heating films. To accommodate the repetitive inspection processes inherent in products with consistent patterns, the system operates at the region level rather than the frame level. By modifying the U-Net architecture, the system achieved precise segmentation of the inspection area, enabling real-time detection of microscale pinholes and scratches. Additionally, a sticker marker was developed to label the defective regions detected on the film. The proposed system was experimentally validated in an actual production environment, where it demonstrated an impressive 96.6% accuracy in area inspection and a 97.5% defect detection rate at a transportation speed of 12 m/min. These results serve as clear evidence of the effectiveness and practicality of the automatic detection capability facilitated by deep learning in production processes.

关键词： Machine vision U-Net deep learning Automated production real-time defect detection Heating film

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