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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping

Learning Embeddings with Centroid Triplet Loss for Object Id...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Anas Gouda Max Schwarz Christopher Reining Sven Behnke Alice Kirchheim TU Dortmund Lamarr Institute for Machine Learning and Artificial Intelligence Autonomous Intelligent Systems - Computer Science VI & Center for Robotics University of Bonn Germany Fraunhofer IML

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Foundation models are a strong trend in deep learning and computer vision. These models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any further object information. When they are followed in a pipeline by an object identification model, they can perform object detection without training. Here, we focus on training such an object identification model. A crucial practical aspect for an object identification model is to be flexible in input size (number of input images). As object identification is an image retrieval problem, a suitable method should handle multi-query multigallery situations without constraining the number of input images (e.g. by having fixed-size aggregation layers). The key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids. CTL yields high accuracy, avoids misleading training signals and keeps the model input size flexible. In our experiments, we establish a new state of the art on the ArmBench object identification task, which shows general applicability of our model. We furthermore demonstrate an integrated unseen object detection pipeline on the challenging HOPE dataset, which requires finegrained detection. There, our pipeline matches and surpasses related methods which have been trained on dataset-specific data. Code and pretrained models are available. 5

关键词： Training Location awareness image segmentation Accuracy Pipelines Object detection Object segmentation Market research Object recognition Proposals

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Design and Optimization of a Dynamic Train image Acquisition System Utilizing Line Scan Camera Technology

Design and Optimization of a Dynamic Train Image Acquisition...

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Advances in Electrical Engineering and Computer applications( AEECA), 2020 IEEE International conference on

作者： Changgeng Li Feng Xu Wei Li Xiaodong Sun Bo Yuan Crrc Qingdao Sifang Co. Ltd Qingdao China

In response to the demand for efficient and high-quality daily maintenance and inspection of modern railway trains, the utilization of AI-based machine vision technology has been proven to significantly enhance maintenance efficiency and quality. image quality plays a critical role in visual inspection. Thus, this study presents the design of a dynamic train exterior image acquisition system based on line scan camera technology. Through a comprehensive analysis of maintenance requirements and business scenarios, the hardware structure and functional framework of the system are meticulously developed. The image acquisition module is strategically deployed in a 360° arrangement to enable panoramic image acquisition of the train’s exterior. Leveraging the imaging principle of the line scan camera, two sets of magnetic cylinders are employed to measure the train’s velocity multiple times, ensuring continuous correction of the image acquisition line frequency for stable image capture. Furthermore, a character recognition algorithm for train numbers is extensively researched, enabling accurate identification of the train number through image acquisition, preprocessing, and OCR recognition algorithms. The proposed system integrates professional image acquisition and processing techniques, enabling high-resolution and high-precision stable acquisition of dynamic train images. It provides crucial data support for precise detection of train exterior conditions and offers reliable technical means for train trajectory monitoring and safety management.

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Enhancing Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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International conference on Advances in Information Technology (ICAIT)

作者： C A Yogaraja C Priyanka Priyadharshini. SP J.Jagan Babu S. Rajagopal S. Poorani Computer Science and Engineering Ramco Institute of Technology Rajapalayam School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus Department of Artificial Intelligence and Machine Learning (AML) St. Joseph's College of Engineering OMR Chennai Department of ECE R.M.D Engineering college Kavaraipettai Department of IT National Engineering College Kovilppati Computer Technology-UG Kongu Engineering College Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species.

关键词： visualization image recognition Insects Biological system modeling Noise Feature extraction image sequences

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visual Measurement of Levitation Gap in Maglev Ball System Based on Pixel Area 16

Visual Measurement of Levitation Gap in Maglev Ball System B...

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16th IEEE conference on Industrial Electronics and applications (ICIEA)

作者： Li, Yifei Zhang, Zhifei Jing, Guoheng Ma, Xianchao Kong, Jie Jing, Yongzhi Southwest Jiaotong Univ Sch Elect Engn Minist Educ Chengdu Peoples R China Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu Peoples R China

ISBN: (纸本)9781665422482

The levitation control system is a key part of maglev train. The measurement of levitation gap plays an important role in the suspension system. Traditional gap sensors have some disadvantages such as high power supply voltage, temperature influence and measurement distance limitation. In this paper, a method is designed on visual measurement of levitation gap. The maglev ball system is taken as the research object. We first obtain the original high-definition images of the ROI(region of interest) in the levitation gap. Then, the pixel area in the ROI of levitation gap is obtained by calculating the number of pixel points whose results are "1" in the binarization image. Finally, the pixel area is calibrated with the actual gap distance and the actual gap distance is calculated. This method calculates the actual gap according to the image pixel area in the ROI of levitation gap. It has high measuring accuracy and strong anti-interference ability, which could effectively remove the asymmetric interference in the ROI of levitation gap, locating and alarming the interfered fault position.

关键词： magnetic levitation gap machine vision image processing calibration

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Application of Advanced Data Mining and Computer vision Techniques in License Plate Recognition

Application of Advanced Data Mining and Computer Vision Tech...

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Smart Technologies, Communication and Robotics (STCR)

作者： Ayorinde Alase Danish Multani Sai Rithwik Amajala Udhaya S K Kamal Upreti Electrical and Computer Engineering University of Arkansas at Little Rock Little Rock Arkansas US UST Global Solutions Gurgaon Haryana India Computer Science and Engineering India Computer Science and Engineering Easwari Engineering College Chennai India Dept. of Computer Science Christ University Delhi NCR India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

As urbanization is expanding rapidly and vehicular traffic is on the rise, efficient and automated vehicle identification is a must. Smart transportation, safety monitoring, & police work all heavily rely on Automatic License Plate Detection (ALPD). Traditional heuristic-based image processing techniques are incapable of handling environmental variations; Artificial intelligence (AI) & machine vision solutions are therefore used. YOLO, Faster R-CNN, and SSD are some of the most effective CNN-based and object identification algorithms that demonstrate cutting-edge accuracy in license plate recognition. The paper studies the usage of deep learning algorithms with prepossessing and advanced localization methods for ALPDs' optical character recognition (OCR) and character segmentation. The research also studies integrating ALPD with edge computing and IoTs to develop real-time smart traffic solutions. It also examines machine learning techniques, deep learning innovations, and conventional methods, emphasizing how well various models perform in comparison in terms of accuracy, computational efficiency, and real-time processing power. By employing cutting-edge designs, this study seeks to increase the scalability and resilience of license plate recognition systems, which will support future urban development, security applications, as intelligent transportation management.

关键词： YOLO Deep learning Computational modeling Lighting Real-time systems Computational efficiency Smart transportation Internet of Things License plate recognition Edge computing

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Deepfake image and video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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SODA: A Real-time Simulation Framework for Object Detection and Analysis in Smart Manufacturing 25

SODA: A Real-time Simulation Framework for Object Detection ...

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25th KES International conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES)

作者： Lasek, Piotr Univ Rzeszow Inst Comp Sci Ul Prof St Pigonia 1 PL-35310 Rzeszow Poland

For modern manufacturing firms, automation has already become a norm but constantly needs to be improved as firms still face strong demand to increase their productivity. This can be achieved by reducing dependability on manpower, reaching lean and even unmanned production and this is where some of the standards of Industry 4.0 come in useful, not to mention: machine vision, image Recognition or machine Learning. In our paper, we present SODA - our approach to build a flexible ML and AI enabled framework for object detection, analysis, and simulation. The framework is designed to support a development process of solutions requiring real-time analysis of images of different types of moving objects on a conveyor belt. In our work we discuss architectural challenges of the developed framework as well as the basic components of the system. We do also provide information on how to use the framework and present a sample implementation of an actual system employing some of the machine learning methods. (c) 2021 The Authors. Published by ELSEviER B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

关键词： Object Detection Real-Time Analysis image processing Industrial applications Object Recognition machine vision Smart Manufacturing Industry 4.0

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Intelligence Pest Detection and Control in Agriculture using Computer vision and Deep Learning

Intelligence Pest Detection and Control in Agriculture using...

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International conference on ent Computing and Control Systems (ICICCS)

作者： S. Ashok Kumar Fahimuddin Shaik B. Yashwitha P. vidyavathi A.Uma Maheswari P. Spurthi Department of AI&DS Annamacharya University Andhra Pradesh Department of ECE Annamacharya University Andhra Pradesh Department of AI&DS Annamacharya Institute of Technology and Sciences Andhra Pradesh

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

One of the most significant challenges in agriculture over the years has been the detection and also control of pests and while it continues to this day, the advent of technology in the agricultural sector is providing solutions that are incredibly promising. Pests cause significant worldwide crop loss and represent a threat to food security and farmer livelihoods. Conventional pest management approaches revolve around labour-intensive strategies, large applications of pesticides, and reactionary measures, resulting in environmental degradation and sub-optimal crop yields. This work utilizes the power of AI(Artificial Intelligence),computer vision and deep learning to create a smart pest detection system. It treats pest identification and damage detection as real-time visual data processing tasks with machine learning approaches, making it possible to swiftly and accurately locate pests and their damage in the photos taken through the camera, facilitating extra prompt preventive measures. Working under the hood, the algorithms adapt to changing environmental conditions and shifting pest behaviours, learning as they go. Most of the pest detection systems today are manual, requiring manual visual inspection and image recognition approaches, this solution not only provides the next level of system but it also paves the way for pest management in the most efficient, accurate & sustainable way.

关键词： Deep learning Computer vision visualization Accuracy Pesticides Manuals Control systems Real-time systems Agriculture Farming

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Data Fusion Approach for Unmodified UAV Tracking with vision and mmWave Radar

Data Fusion Approach for Unmodified UAV Tracking with Vision...

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International conference on Unmanned Aircraft Systems (ICUAS)

作者： Guilherme Amaral João J. Martins Pedro Martins André Dias José Almeida Eduardo Silva INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

The knowledge of the precise 3D position of a target in tracking applications is a fundamental requirement. The lack of a low-cost single sensor capable of providing the three-dimensional position (of a target) makes it necessary to use complementary sensors together. This research presents a Local Positioning System (LPS) for outdoor scenarios, based on a data fusion approach for unmodified UAV tracking, combining a vision sensor and mmWave radar. The proposed solution takes advantage of the radar's depth observation ability and the potential of a neural network for image processing. We have evaluated five data association approaches for radar data cluttered to get a reliable set of radar observations. The results demonstrated that the estimated target position is close to an exogenous ground truth obtained from a visual Inertial Odometry (viO) algorithm executed onboard the target UAV. Moreover, the developed system's architecture is prepared to be scalable, allowing the addition of other observation stations. It will increase the accuracy of the estimation and extend the actuation area. To the best of our knowledge, this is the first work that uses a mmWave radar combined with a camera and a machine learning algorithm to track a UAV in an outdoor scenario.

关键词： visualization Target tracking Radar clutter Airborne radar Data integration Radar imaging vision sensors Radar tracking Autonomous aerial vehicles Millimeter wave communication

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12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

12th Ecuadorian Conference on Information and Communication ...

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12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

ISBN: (纸本)9783031754302

The proceedings contain 24 papers. The special focus in this conference is on Information and Communication Technologies. The topics include: Pancreas Segmentation Using SRGAN Combined with U-Net Neural Network;Using Artificial Intelligence and X-ray images to Train and Predict COviD-19 and Pneumonia: Tool for Diagnosis and Treatment;Region of Interest Features and Classification of MRI Brain Lesions;deep Learning-Based Leukemia Diagnosis from Bone Marrow images;Breast Thermographic image Augmentation Using Generative Adversarial Networks (GANs);Evaluating Histopathological Cancer Detection: A Comparative Analysis of CNN Architectures for Tumor Detection in Lymph Node Pathology;viTSigat: Early Black Sigatoka Detection in Banana Plants Using vision Transformer;automatic Parking Space Segmentation Using K-Means Clustering and image processing Techniques;neural Agents with Continual Learning Capacities;centinela: An Intelligent System Based on an Integrated Architecture for Supporting Scholars;Optimizing Predictive Models in Healthcare Using Artificial Intelligence: A Comprehensive Approach with a COviD-19 Case Study;predictive Model Proposal in Telemetry Using machine Learning Techniques to Anticipate Water Degradation in Aquaculture;Evaluation of the Use of Artificial Intelligence Techniques in the Mitigation of the Broadcast Storm Problem in FANET Networks;adaptation Dynamics of Galápagos Finches: Evolutionary Responses to Climate Variation Explored through machine Learning;incident Alert Priority Levels Classification in Command and Control Centre Using Word Embedding Techniques;physics Informed Neural Networks and Gaussian Processes-Hamiltonian Monte Carlo to Solve Ordinary Differential Equations;Pre-processing of the Text of ECU 911 Emergency Calls;Secure BLE Communication Between Android Devices and Embedded Systems for IoT applications;modeling and Control of a Peltier Thermoelectric System Applying a Multi-objective Optimization Approach;wearable Device for Ac

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