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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

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Optimization of Charging Prediction in Electric Vehicle Fleets: An Integrated Approach with EdgeAI and Data Flow Pipelines

Optimization of Charging Prediction in Electric Vehicle Flee...

引用

ieee Signal processing and communications Applications (SIU)

作者： Enes Sevim Utku Evren Dalgıç Metin Yılmaz Eyüp Çinar Ahmet Yazıcı Bilgisayar Mühendisliği Eskişehir Osmangazi Üniversitesi

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In this study, a system is proposed to optimize the charging prediction process of electric vehicle fleets and is integrated with a data flow pipeline constructed along with an EdgeAI-based charge prediction approach. The system processes the data collected from the vehicles on the EdgeAI unit, predicts the current state of charge, transfers it to database through the Fiware middleware, enabling its visualization with data analytics tools. Proposed for the effective management of battery charge states in electric vehicle fleets, this system aims to make a more effective contribution to environmental sustainability and energy efficiency goals by optimizing the data flow. The system was prototyped and tested using raw data collected through the battery management system of an actual electric vehicle. The results show that the proposed system is reliable and can be easily scaled in fleet scenarios. In the future, it is planned to scale and implement the system for fleet vehicles in real time and conduct stress tests.

关键词： Pipelines Signal processing Electric vehicles Real-time systems visual databases State of charge Reliability

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Task-Driven Deep image Enhancement Network for Autonomous Driving in Bad Weather

Task-Driven Deep Image Enhancement Network for Autonomous Dr...

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Lee, Younkwan Jeon, Jihyo Ko, Yeongmin Jeon, Byunggwan Jeon, Moongu Gwangju Inst Sci & Technol GIST Machine Learning & Vis Lab Gwangju 61005 South Korea Korea Culture Technol Inst KCTI Gwangju 61005 South Korea

ISBN: (纸本)9781728190778

visual perception in autonomous driving is a crucial part of a vehicle to navigate safely and sustainably in different traffic conditions. However, in bad weather such as heavy rain and haze, the performance of visual perception is greatly affected by several degrading effects. Recently, deep learning-based perception methods have addressed multiple degrading effects to reflect real-world bad weather cases but have shown limited success due to 1) high computational costs for deployment on mobile devices and 2) poor relevance between image enhancement and visual perception in terms of the model ability. To solve these issues, we propose a task-driven image enhancement network connected to the high-level vision task, which takes in an image corrupted by bad weather as input. Specifically, we introduce a novel low memory network to reduce most of the layer connections of dense blocks for less memory and computational cost while maintaining high performance. We also introduce a new task-driven training strategy to robustly guide the high-level task model suitable for both high-quality restoration of images and highly accurate perception. Experiment results demonstrate that the proposed method improves the performance among lane and 2D object detection, and depth estimation largely under adverse weather in terms of both low memory and accuracy.

关键词： Training Rain Computational modeling Memory management Computational efficiency Task analysis Autonomous vehicles

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ISVDA: An in Storage processing Accelerator for visual Data Analysis

ISVDA: An in Storage Processing Accelerator for Visual Data ...

引用

ieee International conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Zhenhua Zhao Zhaoyan Shen Xiaojun Cai School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Deep neural networks(DNNs) have been widely applied in visual data analysis. To accelerate DNN-based visual data inference, contemporary visual data analytics systems employ strategies like model specialization and model cascading to alleviate the computational demands of DNN processes. In addition, some existing systems seek to reduce the overhead of data decoding by directly storing decoded RGB data on storage devices, albeit at the expense of significantly increased storage costs and data transfer volumes. In this paper, we introduce ISVDA, a visual data analysis accelerator based on In-storage computing. ISVDA proposes to offload the data-intensive video preprocessing tasks, encompassing data decoding and resizing, to the computational storage device, leveraging its ample internal bandwidth and computational advantages. Concurrently, ISVDA introduces a dynamic sampling algorithm that intelligently adjusts sampling strides based on spatiotemporal information encoded in motion vectors, thereby skipping more video frames for DNN inferences in both retrospective and streaming analysis scenarios. Experiments conducted on real hardware demonstrate that compared to CPU-based preprocessing systems, ISVDA can achieve average 6.8x speedup for image data analysis and 2.8x speedup for video data analysis.

关键词： visualization Data analysis Heuristic algorithms Computational modeling Dynamics Artificial neural networks Streaming media Vectors Data models Decoding

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KOOPI: Keypoint-Oriented Object Positioning in Industry

KOOPI: Keypoint-Oriented Object Positioning in Industry

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2022 International conference on Innovations and Development of Information Technologies and Robotics, IDITR 2022

作者： Zhao, Chonghao Wu, Gang University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu China

ISBN: (纸本)9781665409322

In manufacturing, object detection via industrial images enables many typical applications such as positioning of screw holes, electronic components, and other devices. The conventional method based on classical image processing generally consists of two steps: image feature extraction and object classification, so that it usually results in a low detection speed and poor accuracy due to its complicated procedure. In recent years, thanks to the rapid development of deep learning networks, higher classification accuracy and less computing performance requirement can be achieved in many typical applications, compared to using the conventional schemes. In this paper, by investigating object detection based on deep learning, a new idea utilizing some keypoint-oriented deep learning networks to the workpiece positioning area is proposed and verified by collecting dataset from practical workpieces. Our novel method performs competitively with existing schemes and runs in real-time. As for the simulation of screw hole positioning, the proposed network can effectively detect the screw hole in the image and accurately locate it. By comparing with commercial software such as Halcon® or VisionPro®, the feasibility of applying keypoint-oriented deep learning networks to intelligent manufacturing is validated. © 2022 ieee.

关键词： Object detection

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Remote-controlled Rust Assessment System for Weathering Steel

Remote-controlled Rust Assessment System for Weathering Stee...

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ieee International conference on Mechatronics (ICM)

作者： Watanabe, Hiromi Tanzawa, Tsutomu Kotani, Shinji Shimizu, Tsuyoshi Univ Yamanashi Dept Interdisciplinary Res Grad Sch Div Engn Kofu Yamanashi Japan

ISBN: (纸本)9781728144429

Weathering steels possess excellent corrosion resistance due to the precise protective rust layer formed on the surface, which suppresses further corrosion. Due to its superior weatherability, weathering steels are used for bridges, external walls of buildings, and outer surfaces of railroad vehicles. However, weatherability cannot fully inhibit corrosion due mainly to a high salt level in the atmosphere. Therefore, the rust condition of weathering steels must be regularly inspected and evaluated. Currently, the rust conditions of weathering steels are assessed by visual inspections. The drawbacks of this method are that it requires inspectors with expertise and lacks accuracy and repeatability as it is a subjective assessment. We have developed a system capable of quantitatively assessing rust conditions of steels. Previously we proposed a method to evaluate rust conditions of weathering steels by capturing images of rust with multiple lighting devices and cameras, and subsequently processing the captured images. In that study, we fabricated an image capturing device and confirmed that rust conditions of weathering steels could be evaluated accurately. Herein we report a modified image capturing device, which is smaller, lighter, and can be operated remotely. Our modified image capturing device can travel upwards, downwards, and horizontally. Additionally, it can be operated by a remote control from 80-m away.

关键词： rust assessment weathering steel remote-controlled system image processing

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8th International conference on Computer Vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on Computer Vision and image processing, CVIP 2023

ISBN: (纸本)9783031585340

The proceedings contain 140 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity;MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement;feature Fusion and Multi-head Attention Based Hindi Captioner;Payload Length and Location Identification Using a Novel CNN and Re-embedding Strategy in Stego images Created by Content Adaptive Steganographic Algorithms;ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions;driver Drowsiness Detection Using Vision Transformer;automated Cricket Commentary Generation for Videos;image Captioning with visual Positional Embedding and Bi-linear Pooling;Cartilage Segmentation from MRI images Towards Prediction of Osteoarthritis;Reinforcement Algorithm-Guided ROI Extraction of Fingerprint Biometric Data;improved Metric Space for Shape Correspondence;an Integrated Approach: Combining GrabCut and Contour-Matching for Hand Gesture Segmentation in Indian Sign Language;identity Preserved Expressive Talking Faces with Synchrony;isolated Sign Language Recognition Using Deep Learning;a Gradient-Based Approach to Interpreting visual Encoding Models;adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation;Efficient Seizure Prediction from images of EEG Signals Using Convolutional Neural Network;coping with Increased Levels of Label Noise in Facial Expression Recognition;experimental Evaluation of Needle Tip Prediction Using Kalman Filtering Approach;a Deep Face Antispoofing System with Hardware Implementation for Real-Time Applications;Bit Plane Segmentation and LBP-Based Coverless Video Steganography for Secure Data Transmission;a Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear images Using

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Navigating Beyond GPS: Horizon Detection for Autonomous Navigation

Navigating Beyond GPS: Horizon Detection for Autonomous Navi...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Alastair Finlinson Sotiris Moschoyiannis Björn Holmberg Department of Computer Science University of Surrey Surrey UK Space and Defence SAAB Defence Sweden Upsala Sweden

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In a scenario where a GPS signal is unavailable, navigation becomes more challenging. To enable precise navigation, visual features of the surroundings can be interpreted to direct vessels. We propose to use the visible horizon line as a novel solution. The visible horizon line is constructed from features at the intersection between the sky, land masses, and the waterline. Accurate extraction relies on a robust model operating in adverse weather and equipment conditions. We propose to tackle this new problem with an approach based on semantic boundary prediction with a framework that primarily looks at deep supervision for feature conditioning and differential edge detection to incorporate strong priors early in the training process. We also use semantic segmentation as a support task to provide a strong supervision signal and features to the semantic boundaries prediction task. We conduct experiments on the dataset to evaluate the model in various scenarios. Our results show that our framework predicts high-quality boundaries and segmentation masks over various datasets and domains with the ability to perform in low-data scenarios. We demonstrate that the combination of the labelling and the edge priors improve over the baseline by 32-44% on the semantic boundary estimation task.

关键词： Training visualization Navigation Semantic segmentation image edge detection Semantics Feature extraction Labeling Global Positioning System Autonomous robots

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Subjective Fidelity Assessment of Audio- and Video-Driven Talking Head Generation Methods

Subjective Fidelity Assessment of Audio- and Video-Driven Ta...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Anthony Trioux Yusong Gao Jiarun Song Wenjie Wu Faming Ma Fuzheng Yang School of Telecommunications Engineering Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Audio- and Video-Driven Talking Head Generation methods have attracted considerable research interest due to recent advances in Artificial Intelligence Generated Content (AIGC) technologies. In such approaches, a single image is artificially animated by leveraging audio and/or motion features extracted from video sources. Despite notable progress, current performance assessments rely primarily on traditional objective metrics, often neglecting subjective evaluation aspects. To address this issue, we propose in this paper a subjective fidelity assessment of recent Audio- and/or Video-Driven Talking Head Generation methods. This study aims to assess how accurately and convincingly the generated video reproduces the visual and behavioral characteristics of a real human face, as well as how closely the video aligns with expected natural human expressions, movements, and/or audio synchronization. In order to provide a detailed assessment of the fidelity in the context of talking heads, our study focuses on six key criteria: Overall Fidelity, Gaze Fidelity, Audio-Video Sync Fidelity, Head Pose Fidelity, Expression Fidelity, and Overall visual Quality. Experiments results reveal a nuanced picture of the fidelity in this context, where the performance varies significantly depending on the video content itself as well as how the animation is generated, highlighting the needs for further research. This research represents an initial step towards the evaluation of Audio- and Video-Driven generative image animation methods for Talking heads while offering insights for improving the accuracy and realism of those techniques. The dataset and corresponding results are available at https://***/a-trioux/Subjective-Fidelity-Assessment-Talking-Head.

关键词： Measurement visualization Head Signal processing Feature extraction Animation Behavioral sciences Synchronization Speech processing Faces

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Joint Representation of Temporal image Sequences and Object Motion for Video Object Detection

Joint Representation of Temporal Image Sequences and Object ...

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ieee International conference on Robotics and Automation (ICRA)

作者： Koh, Junho Kim, Jaekyum Shin, Younji Lee, Byeongwon Yang, Seungji Choi, Jun Won Hanyang Univ Dept Elect Engn Seoul 04763 South Korea SK Telecom T3K Vis AI Prod Ctr Excellence Seongnam 13487 South Korea

ISBN: (纸本)9781728190778

In this paper, we propose a new video object detection (VoD) method, referred to as temporal feature aggregation and motion-aware VoD (TM-VoD), that produces a joint representation of temporal image sequences and object motion. The TM-VoD generates strong spatio-temporal features for VOD by temporally redundant information in an image sequence and the motion context. These are produced at the feature level in the region proposal stage and at the instance level in the refinement stage. In the region proposal stage, visual features are temporally fused with appropriate weights at the pixel level via gated attention model. Furthermore, pixel level motion features are obtained by capturing the changes between adjacent visual feature maps. In the refinement stage, the visual features are aligned and aggregated at the instance level. We propose a novel feature alignment method, which uses the initial region proposals as anchors to predict the box coordinates for all video frames. Moreover, the instance level motion features are obtained by applying the region of interest (RoI) pooling to the pixel level motion features and by encoding the sequential changes in the box coordinates. Finally, all these instance level features are concatenated to produce a joint representation of the objects. Experiments on the imageNet VID dataset demonstrate that the proposed method significantly outperforms existing VoDs and achieves performance comparable with that of state-of-the-art VoDs.

关键词： visualization Object detection Logic gates Performance gain Feature extraction Encoding image sequences

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An Accurate Segmentation Method for Experimental image of Cells 7

An Accurate Segmentation Method for Experimental Image of Ce...

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7th International conference on Computer and communications, ICCC 2021

作者： Li, Shoucong Chen, Weiyang School of Computer Science and Technology Jinan China

ISBN: (纸本)9781665409506

image segmentation is not only a basic problem in the field of image processing, but also in the field of biological data processing. In recent years, people have made a lot of efforts to improve the accuracy of segmentation. This paper proposes an image segmentation method based on generative adversarial networks to segment the images of human HT29 colon cancer cells. The network uses Unet as the generator network. The segmentation results of the human HT29 colon cancer cells image dataset show that compared with other methods, this method improves the average accuracy of image segmentation to 98.659%. © 2021 ieee.

关键词： image segmentation

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