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检索条件"任意字段=9th IEEE International Enterprise Distributed Object Computing Conference"

共 1225 条记录，以下是21-30 订阅

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Enhacing Logistics with Computer Vision and Fog computing-Driven Auto-ID Technologies 9

Enhacing Logistics with Computer Vision and Fog Computing-Dr...

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9th ieee international conference on Fog and Mobile Edge computing (FMEC)

作者： Losada del Olmo, Juan Jesus Perales Gomez, Angel Luis de Teruel Alcolea, Pedro E. Lopez Ruiz Garcia, Alberto Garcia Clemente, Felix J. Univ Murcia Fac Comp Sci Murcia Spain Bleecker Technol Murcia Spain

ISBN: (纸本)9798350366495;9798350366488

In this paper we propose a series of enhancements to a computer vision system specifically designed for auto-identification (Auto-ID) of loads in industrial and logistic settings using a fog computing architecture. the system effectively monitors multiple docks within a logistics warehousing management system, identifying loads on pallets in real-time and providing immediate feedback to ensure proper truck loading. Its Auto-ID functionality has been expanded to include visual estimation of the class and approximate size of pallets, resulting in an efficient method for automatic image labeling that enables the creation of few-shot training datasets. Our experimental results show that these datasets are sufficient for leveraging on state-of-the-art foundational computer vision models to obtain high accuracy and precision rates on both object classification and load size estimation by precise bounding box placement. Furthermore, our evaluation also demonstrates that employing simple techniques like linear probing significantly reduces the need for extensive processing and cloud-based training, perfectly aligning with the system's underlying fog computing architecture. this streamlined approach simplifies parameter calibration, eliminates the need for extensive data uploads, and facilitates flexible online updates as new load types and class samples are integrated into the system.

关键词： Computer Vision Fog computing Logistics Auto-identification Foundational Models Few-shot Learning Linear Probing

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PTZ Camera Teams for Effective, Close In Multi object Detection and Tracking 9

PTZ Camera Teams for Effective, Close In Multi Object Detect...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Bano, Gulafsa Bharti, Dheeraj Saha, Parikshit Gaurav, Kumar Venkatesh, K.S. Harcourt Butler Technical University Kanpur India Indian Institute of Technology Kanpur India

ISBN: (纸本)9798350385991

In this study, we introduce the concept of a camera team for real-time, multi-object detection and tracking using pan-tilt-zoom (PTZ) cameras. Traditionally, the use of PTZ cameras for the detection and tracking of multiple objects has been constrained by the zoom-field of view (FOV) trade-off. Detecting and tracking two distant objects typically necessitated a broader FOV, resulting in a reduction in zoom capabilities. the proposed camera team comprises multiple PTZ cameras with nearly coincident optical centers, where each camera is dedicated to tracking a single target exclusively. We employ YOLOv7 and DeepSORT algorithms on individual cameras for target detection and tracking, respectively. Additionally, an auxiliary PTZ camera can be utilized to provide an overarching view of all targets, if required. the central control code orchestrates all camera movements in a synchronized fashion, facilitating various operational modes including’chase’,’solo track’, and’correlate’. Experimental results demonstrate that this camera team architecture significantly enhances the efficiency of object detection and tracking across expansive areas, providing high-resolution feeds of multiple targets concurrently-a feat unattainable with traditional camera setups. © 2024 ieee.

关键词： Ultrasonic cameras

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Voice Assisted object Detection For Visually Impaired 9

Voice Assisted Object Detection For Visually Impaired

引用

9th ieee international conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Hitaish, K.G. Krishnaswamy, Vani Vishnu, M. Mahima, B. Reva University School of Cse Bengaluru India

Visual impairment can have a severe negative effect on a person9;s freedom, employment, and daily life. Globally there are at least 2.2 billion individuals who experience some form of visual impairment. Reading, wr... 详细信息

ISBN: (纸本)9798350334395

Visual impairment can have a severe negative effect on a person's freedom, employment, and daily life. Globally there are at least 2.2 billion individuals who experience some form of visual impairment. Reading, writing, and navigating their environment is challenging for people with visual impairment. A lower quality of life as well as feelings of loneliness and sadness may result from this. there are a variety of deep learning object detection models based on computer vision. In this paper, we aim to integrate MobileNet SSD architecture to develop a navigation system which provides faster and accurate object detection to assist the visually impaired. this project is a mobile-friendly architecture that is lightweight and optimized for embedded and mobile devices with constrained CPU power. In this paper we also designed algorithms which provides direction and distance along with audio output. In the future works, this project can be further modified and implemented in various domains. © 2023 ieee.

关键词： object detection

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L-RTDETR: A Lightweight Real-Time object Detection Algorithm for Defect Detection 9

L-RTDETR: A Lightweight Real-Time Object Detection Algorithm...

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9th international conference on Image, Vision and computing, ICIVC 2024

作者： Chen, Xinran Li, Shufeng Yang, Wei School of Information and Communication Engineering Communication University of China Beijing China School of Electronic Information Engineering Beihang University Beijing China

ISBN: (纸本)9798350385991

In the current deep learning research landscape, image processing technology faces significant challenges in lightweight defect detection despite its broad application in object detection. this study introduces the Lightweight Real-Time DEtection TRansformer (L-RTDETR) model, achieving a balance between high accuracy and model lightness. the model employs a Large Selective Kernel Network (LSKNet) based lightweight backbone, reducing network size and parameters. It also integrates Deformable Attention (DAttention) based Intra-scale Feature Interaction to enhance detection speed with a sparse attention mechanism. By combining L1, Generalized Intersection over Union (GIoU), and Normalized Wasserstein Distance (NWD) loss functions with weighted metrics, the model’s adaptability for object detection tasks improves. Experimental results on the Northeastern University Defect Detection dataset show that L-RTDETR achieves a 35.8% reduction in model size and increases detection speed to 61.6fps, outperforming the conventional RTDETR algorithm. It also shows a 2.5% improvement in mean Average Precision at 0.5 (mAP@0.5) and a 1.7% increase in mAP@0.5-0.95. these results underscore the model’s efficiency in lightweight defect detection. © 2024 ieee.

关键词： object detection

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object Oriented Fuzzy Decision Model for Insurance Claim 9

Object Oriented Fuzzy Decision Model for Insurance Claim

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9th ieee international conference on computing, Engineering and Design, ICCED 2023

作者： Susanto, Sumiatie Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

the insurance industry faces a significant challenge concerning insurance claims, particularly due to the prevalence of fraudulent insurance claims. To address this issue, one potential solution is the implementation of a computer-based decision model. this research presents a fuzzy decision model based on object-oriented method development. the study involves seven stages (i.e. case analyzing, parameter analyzing, objects-parameters linking, detail object relation constructing, parameter exchange analyzing, OOFDM constructing, and model verifying and validating), with an object-oriented approach serving as the foundational method for constructing the model, and fuzzy logic as the primary method for assessing claim risks in proposing the best decision. the model has the capability to simulate insurance claims and offers objective decisions based on 19,611 claims data, categorizing them into two decision categories: acceptance and pending. © 2023 ieee.

关键词： Fuzzy logic

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A Machine Learning Approach to Unusual object Detection in Faint Satellite Imagery 9

A Machine Learning Approach to Unusual Object Detection in F...

引用

9th international conference on Smart computing and Communications, ICSCC 2023

作者： Sreeja, C.K. Ameenudeen, P.E. College of Engineering Trivandrum Kerala Thiruvananthapuram India College of Engineering Trivandrum Department of Electronics and Communication Kerala Thiruvananthapuram India

ISBN: (纸本)9798350314090

the images taken by by satellites contain different artifacts with varying size. these images are used as input for many applications. the analysis of satellite images are based on human interpretation and this creates limitations. object detection from aerial images are challenging as manual detection of objects are infeasible and time consuming. It requires automatic detection of various features and objects from the satellite images. Automated detection systems are designed to run in computing systems with high computing power. A Convolution Neural Network architecture optimized for embedded systems is presented here. Bottom up strategy is adopted and the design starts from small architecture and gradually build into a larger architecture till the design objectives are met. © 2023 ieee.

关键词： object detection

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Parallel Channel Separate Attention Network for Concealed object Detection in Millimeter-Wave Images 9

Parallel Channel Separate Attention Network for Concealed Ob...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Li, Tang Chen, Zhenhong Wen, Xin Chen, Liang Zhang, Shengkang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Radio Metrology and Measurement Beijing China

ISBN: (纸本)9798350376548

the active millimeter-wave scanner plays an increasingly pivotal role in public safety by employing a non-contact method to detect contraband concealed beneath human clothing. However, millimeter-wave images encounter challenges such as low signal-to-noise ratio, limited resolution, and suspicious targets being small in size, when compared to optical images. To address these challenges and improve detection performance, this paper introduces a Parallel Channel Separate Attention (PCSA) method. Specifically, we propose a parallelized and channel-separated attention module to enhance the extraction ability of features pertaining to minute targets. this module is integrated into the YOLOv8 backbone network, namely PC SA-YOLO. Moreover, we incorporate high-frequency information from the original image into the network input to provide preliminary guidance. Extensive experiments conducted on millimeter-wave datasets demonstrate that our proposed method outperforms existing state-of-the-art detection techniques. © 2024 ieee.

关键词： Hosiery manufacture

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Multi-scale Feature Alignment Aggregation Method for Enhanced object Detection in UAV Images 9

Multi-scale Feature Alignment Aggregation Method for Enhance...

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9th international conference on Image, Vision and computing, ICIVC 2024

作者： Liu, Shanshan Tao, Huamin Shen, Xinglin Xiao, Shanzhu Hu, Shuming Yao, Jiping College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350385991

object detection in Unmanned Aerial Vehicles (UAV) images faces numerous challenges, including multi-scale changes, complex scenes, and distant small targets. Existing multiscale feature fusion methods integrate feature information across different levels to address the issues arising from multi-scale variations. However, the integration often lacks interactive information exchange due to the use of concatenation operations, leading to suboptimal results. In response to these challenges, we have enhanced the general-purpose object detection algorithm YOLOv8 by introducing a novel multi-scale feature alignment aggregation method. the Feature Alignment Aggregation Module (FAAM) in the neck pyramid network addresses misalignment and degradation from fusing multi-resolution features, thereby improving target localization across various scales. Additionally, we incorporate the Convolutional Block Attention Module (CBAM) attention mechanism to enhance feature fusion. Furthermore, our Bidirectional Path Aggregation Network (BPAN) and additional small target detection head enhance multiscale feature fusion capabilities. Experimental results on the UAVDT dataset demonstrate that our method outperforms other YOLO series methods with an average precision mAP50 of 34%, indicating superior detection efficacy in scenes with multi-scale variations. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Performance, Energy and Parallelism: Using Near Data Processing in Utility and Cloud computing 15

Performance, Energy and Parallelism: Using Near Data Process...

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ieee/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Agha, Gul Mukherjee, Dipayan Sandur, Atul Univ Illinois Dept Comp Sci Urbana IL 61801 USA

ISBN: (纸本)9781665460873

Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. the paper describes our work building on ideas based on the Actor model of computation. these include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. the paper will conclude with a presentation with a summary of open research problems.

关键词： edge computing actor model energy efficiency distributed computation

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Area Mapping via Pseudo-Label Discovery for Weakly Supervised object Detection 9

Area Mapping via Pseudo-Label Discovery for Weakly Supervise...

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9th ieee international conference on Progress in Informatics and computing, PIC 2024

作者： Chen, Jiawen Song, Yonghong Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350363203

Weakly supervised object detection (WSOD) is a task that uses only image-level category labels to train an object detector. the most common weakly supervised object detection framework uses 'argmax' as a baseline to filter spatially adjacent pseudo-labels, resulting in poor-quality pseudo-labels. In addition, the pseudo-label screening method based on IoU will lose many potential high-quality labels. To address the above problems, we propose a novel weakly supervised object detection method. We design a pseudo-label benchmark determination method, called area mapping, to determine the pseudo-label benchmark through the feature distribution of image and region proposals and improve the overall quality of pseudo-labels. We further propose a similarity-based pseudo-label discovery strategy utilizing the spatial similarity between pseudo-labels to discover high-quality pseudo-labels. Experiments were conducted on public datasets and new state-of-the-art results were obtained on VOC07 and surpassed the current state-of-the-art methods with a slight advantage on the COCO14 dataset. © 2024 ieee.

关键词： Self-supervised learning

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