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deep and Fast Approximate Order Independent Transparency

COMPUTER GRAPHICS FORUM 2024年第6期43卷

作者： Tsopouridis, Grigoris Vasilakis, Andreas A. Fudos, Ioannis Univ Ioannina Dept Comp Sci & Engn Ioannina Greece Athens Univ Econ & Business Dept Informat Athens Greece Phasmatic Corfu Greece

We present a machine learning approach for efficiently computing order independent transparency (OIT) by deploying a light weight neural network implemented fully on shaders. Our method is fast, requires a small constant amount of memory (depends only on the screen resolution and not on the number of triangles or transparent layers), is more accurate as compared to previous approximate methods, works for every scene without setup and is portable to all platforms running even with commodity GPUs. Our method requires a rendering pass to extract all features that are subsequently used to predict the overall OIT pixel colour with a pre-trained neural network. We provide a comparative experimental evaluation and shader source code of all methods for reproduction of the experiments. We present a machine learning approach for efficiently computing order independent transparency (OIT) using a lightweight neural network implemented using shaders. Our method is fast, requires a small constant amount of memory, is more accurate compared to previous approximate methods, works for every scene and is portable to all platforms. image

关键词： rendering visibility determination order-independent transparency real-time rendering deep learning

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RT-CBAM: Refined Transformer Combined with Convolutional Block Attention Module for Underwater image Restoration

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SENSORS 2024年第18期24卷 5893页

作者： Ye, Renchuan Qian, Yuqiang Huang, Xinming Jiangsu Univ Sci & Technol Sch Ocean Engn Dept Elect Informat Engn Zhenjiang 212003 Peoples R China

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, they lag behind CNNs in handling fine local features, especially when dealing with underwater images containing complex and delicate structures. In order to tackle this challenge, we propose a refined transformer model by improving the feature blocks (dilated transformer block) to more accurately compute attention weights, enhancing the capture of both local and global features. Subsequently, a self-supervised method (a local and global blind-patch network) is embedded in the bottleneck layer, which can aggregate local and global information to enhance detail recovery and improve texture restoration quality. Additionally, we introduce a multi-scale convolutional block attention module (MSCBAM) to connect encoder and decoder features;this module enhances the feature representation of color channels, aiding in the restoration of color information in images. We plan to deploy this deep learning model onto the sensors of underwater robots for real-world underwater image-processing and ocean exploration tasks. Our model is named the refined transformer combined with convolutional block attention module (RT-CBAM). This study compares two traditional methods and six deep learning methods, and our approach achieved the best results in terms of detail processing and color restoration.

关键词： deep learning transformer image restoration

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Recent Advances in deep-learning-Based SAR image Target Detection and Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6884-6915页

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yang, Huizhang Yin, Junjun Yang, Jian Martorella, Marco Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Pisa Dept Informat Engn I-56127 Pisa Italy

Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

关键词： Radar polarimetry Target recognition Synthetic aperture radar Marine vehicles Feature extraction Object detection Aircraft Surveys Accuracy deep learning Automatic target recognition change detection (CD) deep learning (DL) SAR image interpretation target detection

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Lightweight deep learning-based automated concrete surface inspection: Crack classification and pixel-level segmentation

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SMART STRUCTURES AND SYSTEMS 2025年第3期35卷 163-175页

作者： Yang, Dong Cai, Yuan-Yuan Xu, En-Dian Zhang, Jing Yuan, Ye Wang, Yan-Jia Guangzhou Univ Earthquake Engn Res & Test Ctr EERTC Guangzhou Peoples R China Guangzhou Univ Key Lab Earthquake Resistance Earthquake Mitigat & Struct Safety Minist Educ Guangzhou Peoples R China Hefei Univ Technol Dept Civil Engn Hefei Anhui Peoples R China Jinan Univ Dept Mech & Construct Engn Guangzhou Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China

Crack detection is an important measure in the field of structural health monitoring. However, visual crack detection is labor-intensive, time-consuming, inefficient, and expensive. Although image-based detection and processing provides an efficient way for structural crack detection, its accuracy depends on image quality. For engineering structures, especially bridges, the change of light conditions and the difference of surface characteristics of structural components pose a major challenge to traditional crack detection methods. In this paper, a novel crack detection method based on convolutional neural networks is proposed. The development of this method is divided into the following stages. The initial automated crack classification is carried out by using MobileNetV3, and then the improved deepLabv3+ network is used to segment the classified crack image semantically accurately. Finally, the real crack image is used for verification. To verify the proposed method, several conventional deep learning networks are trained and compared. The improved deepLabV3+ integrates MobileNetV3 as its feature extraction backbone and incorporates the convolutional block attention module, which achieves 87.79% average intersection and 93.87% average pixel accuracy on public and real data sets. Compared with traditional models such as VGG16, the proposed method shortens the training time by more than 80% while maintaining high detection accuracy. In addition, the compact parameter configuration and moderate model size make it particularly suitable for deployment on mobile detection devices.

关键词： attention mechanism crack detection improved deepLabv3+ lightweight deep learning semantic segmentation

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Construction and real time Update Mechanism of a Multi User Personalized Electricity Consumption Behavior Model Under deep learning 2

Construction and Real Time Update Mechanism of a Multi User ...

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2nd IEEE International Conference on image processing and Computer Applications, ICIPCA 2024

作者： Gao, Chen Zhan, Shian Lin, Yinrong Zhu, Enguo Liu, Yan Zheng, Xiaohui Hong, Shouli State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fujian Fuzhou350003 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9798350360240

It is a troublesome problem to model the user's electricity consumption behavior and add an update mechanism. Traditional models have challenges in real-time and personalization, so the introduction of real-time update mechanism has become a key method to improve model performance. Based on deep learning technology, this research constructs a multi-user personalized electricity consumption behavior model, and introduces a real-time update mechanism. First, a large number of user electricity consumption behavior data is used for training to establish the predictive power of the model, and then through the introduction of a real-time update mechanism, the model can respond to new data in a timely manner, and perform parameter updates and prediction result generation. Through experiments, it is evaluated that the prediction accuracy rate of this method is between 89% and 98%. The electricity consumption behavior model with a real-time update mechanism is better than the model without a real-time update mechanism in terms of prediction accuracy and real-time performance. © 2024 IEEE.

关键词： Electricity Consumption Behavior Feature Extraction real time Update Mechanism RNN

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Evolutionary Grid Optimization and deep learning for Improved In Vitro Cellular Spheroid Localization

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9476页

作者： Schurr, Jonas Janout, Hannah Haghofer, Andreas Fuersatz, Marian Scharinger, Josef Winkler, Stephan Nuernberger, Sylvia Univ Appl Sci Upper Austria Bioinformat Res Grp Softwarepark 11-13 A-4232 Hagenberg Austria Johannes Kepler Univ Linz Dept Comp Sci Altenberger Str 69 A-4040 Linz Austria Ludwig Boltzmann Inst Traumatol AUVA Austrian Cluster Tissue Regenerat Res Ctr Cooperat A-1200 Vienna Austria Med Univ Vienna Div Trauma Surg Dept Orthoped & Trauma Surg A-1090 Vienna Austria Johannes Kepler Univ Linz Inst Computat Percept Altenberger Str 69 A-4040 Linz Austria

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created

关键词： heuristic optimization evolution strategy computer vision deep learning machine learning image processing spheroids quantification high-throughput screening

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Clamp Dot image Classification Using Neural Network

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SENSORS AND MATERIALS 2024年第4期36卷 1431-1439页

作者： Srikam, Krittapak Saenthon, Anakkapon King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Robot & AI Engn 1Soi Chalongkrung 1Chalongkrung St Bangkok 10520 Thailand

In this paper, we discuss the classification of images captured by a machine camera while assembling components. To crop out specific points of interest, we employ image processing. Additionally, we utilize deep learning techniques, specifically convolutional neural networks, to identify the type of equipment being assembled. This approach allows us to determine and record specific parts within a device. However, the main challenge of this project is to achieve both high accuracy and the shortest possible prediction time.

关键词： CNN OpenCV image processing TensorFlow neural network

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real-time deconvolution of light fields through pixel selection in the point-spread-function and direct inversion of the image formation process

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APPLIED OPTICS 2025年第3期64卷 578-587页

作者： Eckstein, Viktor Schmid-schirling, Tobias Carl, Daniel Wallrabe, Ulrike Fraunhofer Inst Phys Measurement Tech IPM Freiburg Germany Univ Freiburg Dept Microsyst Engn IMTEK Lab Microactuators Freiburg Germany

This paper explores the optimization of light field deconvolution, a key process in image processing that reconstructs a 3D object space or a 2D refocus plane from a light field. Despite the critical role of deconvolution in light field technology, existing methods are often slow, computationally intensive, and unsuitable for real-time processing. Existing algorithms, such as the Richardson-Lucy approach, while groundbreaking, still suffer performance limitations due to their iterative nature and high computational costs. Central to our approach is the strategic selection of influential pixels within the point-spread-function, reducing redundant computations by focusing only on pixels contributing to a significant portion of the point-spread-function's total intensity. In addition, we explore the potential to directly invert the image formation model, bypass iterative computations, and further accelerate the deconvolution process. Our findings reveal notable improvements in computational efficiency, with some of our methods achieving real-time performance. The reconstruction quality, measured using metrics such as the mean squared error, remained comparable to existing approaches, indicating a favorable balance between speed and reconstruction quality. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Biomedical imaging deep learning Microlens arrays Neural networks Optical design software Plenoptic imaging

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An Enhanced Lung Cancer Detection Approach Using Dual-Model deep learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

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Two-stage deep learning-based video image recognition of early fires in heritage buildings

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Gao, Shenyuan Huang, Guozhong Chen, Xiaolong Jiang, Huiling Liang, Zhou Gao, Xuehong Univ Sci & Technol Beijing Res Inst Macrosafety Sci Beijing Peoples R China Huawei Technol CO LTD Guangzhou Peoples R China

Fire poses a huge safety risk to heritage or historic buildings. Traditional approaches are unsuitable because of their high false alarm rates, delayed features, and vulnerability in heritage building environments. To reduce the serious consequences caused by fires, it is necessary to develop advanced approaches to achieve real-time early fire detection in heritage buildings. Hence, this study proposed a two-stage deep learning-based video image recognition of real-time fires in heritage buildings. Motion detection based on a Gaussian mixture model was applied to determine moving objects in the first stage. Then, a Fire-Det model for the indoor environment and a Fire-Det Nano model for outdoor areas were developed in the second stage. To validate the proposed models, a dataset including negative images was built and tested. To illustrate the performance of this study, the proposed approaches were compared with traditional methods. It is found that the Fire-Det model can complete fire detection at the fastest reasoning speed without losing generalizability of accuracy. The proposed Fire-Det Nano model can also drastically reduce computation time at the expense of a slight reduction in accuracy. Fire-Det is the innovation in AI. Fire-Det Nano and motion detection are applications in engineering. These findings indicate that the proposed method has great potential significance in the application of early-fire detection in heritage buildings, such as the Forbidden City.

关键词： Fire detection Heritage buildings deep learning Motion detection

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