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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： J. A. Syed A. A. Takriti R. R. Damindarov dept. of Robotics & Computer vision Innopolis University Innopolis Russia

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

With the advent of the Industry 4.0, the capability to automatically detect and measure the liquid levels and volumes in the transparent containers (i.e. test tubes in a laboratory) in real-time is a crucial part in the whole perception system of an autonomous robot/system. And by the use of the state-of-the-art computer vision and deep learning technology we can easily help solve this problem. This task of liquid level detection and measurement in transparent containers is a crucial task in various industries, including pharmaceuticals, manufacturing, and chemical processing especially in automated oil testing laboratories, where autonomous systems require precise perception capabilities. This paper explores the potential of computer vision and deep learning to accurately measure the liquid level and volume in transparent containers. Our approach is implemented by training a deep learning model, especially YOLOv8 (You Only Look Once version 8) model trained to recognize features like container type, liquid surface and height. After identifying these key features, our proposed algorithm uses them to correct the errors in the liquid height due to perspective distortion and get the liquid level followed by liquid volume accurately in real-time in a given transparent container. Our approach achieved an accuracy of 98.57 percent for transparent liquid and 98.12 percent for colored liquid, demonstrating the system's potential for practical applications particularly in various automated laboratory testing industries.

关键词： Deep learning Industries computer vision Liquids Volume measurement Computational modeling Containers

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Real-Time Multi-Object Tracking using YOLOv8 and SORT on a SoC FPGA

arXiv

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arXiv 2025年

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © 2025, CC BY.

关键词： System-on-chip

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Towards Single Point Incremental Forming Accuracy: An Approach for the Springback Effect Compensation 19

Towards Single Point Incremental Forming Accuracy: An Approa...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Shaker, Walid K. Klimchik, Alexandr Institute of Robotics and Computer Vision Innopolis University Republic of Tatarstan 420500 Russia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

ISBN: (纸本)9798350320695

The springback effect is a common occurrence in incremental forming, where the formed workpiece elastically deforms and slightly shifts from the desired shape after the tool is released. This phenomenon causes an error between the target and obtained shape, leading to reduced geometric accuracy. It is is a significant challenge in incremental forming and it is a reason why the process has lower accuracy compared to conventional forming methods. This paper presents an off-line springback effect compensation model aiming to generate an optimized toolpath that accounts for the material springback effect. The model is based on an off-line numerical simulation conducted on Abaqus/CAE software. The results demonstrated that the proposed model can effectively reduce the error between the desired and obtained shape by 31.8% for aluminum, 63.2% for copper, and 63.1 % for magnesium. © 2023 IEEE.

关键词： ABAQUS

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A Comparative Analysis of Visual and Point Cloud-Based Place Recognition Methods in Indoor Environment

A Comparative Analysis of Visual and Point Cloud-Based Place...

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International Conference on Advanced robotics (ICAR)

作者： Ivan Efremov Ramil Khafizov Ramil Khusainov Robotics and Computer Vision Innopolis University Innopolis Russia

Place recognition plays a critical role in Simultaneous Localization and Mapping (SLAM) systems, as it helps in identifying previously visited locations and reducing localization errors. In this paper, we present a comprehensive evaluation of various place recognition methods, including DBoW2, Super-Point + SuperGlue, Scan Context, and LoGG3D-Net, using data collected from indoor office and laboratory environments. Our evaluation focuses on the performance of these methods in terms of precision, recall, and F1 score, as well as their computational efficiency. We also explore the benefits of combining different methods to improve loop closure detection by leveraging complementary information from both visual and point cloud data. Our results indicate that vision-based methods generally exhibit better precision, while fusing methods leads to more robust performance. The evaluation code used in this study is available at our GitHub repository 1 1 https://***/4ku/Place-recognition-evaluation. This comprehensive evaluation can serve as a valuable resource for researchers and practitioners working on SLAM and place recognition tasks, as well as inspire the development of novel approaches for improved performance and computational efficiency.

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Going Smaller: Attention-based models for automated melanoma diagnosis

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computers in Biology and Medicine 2025年 185卷 109492-109492页

作者： Nazari, Sana Garcia, Rafael Computer Vision and Robotics Group University of Girona Plaça de Sant Domènec 3 Girona17004 Spain

Computational approaches offer a valuable tool to aid with the early diagnosis of melanoma by increasing both the speed and accuracy of doctors’ decisions. The latest and best-performing approaches often rely on large ensemble models, with the number of trained parameters exceeding 600 million. However, this large parameter count presents considerable challenges in terms of computational demands and practical application. Addressing this gap, our work introduces a suite of attention-based convolutional neural network (CNN) architectures tailored to the nuanced classification of melanoma. These innovative models, founded on the EfficientNet-B3 backbone, are characterized by their significantly reduced size. This study highlights the feasibility of deploying powerful, yet compact, diagnostic models in practical settings, such as smartphone-based dermoscopy, and in doing so revolutionizing point-of-care diagnostics and extending the reach of advanced medical technologies to remote and under-resourced areas. It presents a comparative analysis of these novel models with the top three prize winners of the International Skin Imaging Collaboration (ISIC) 2020 challenge using two independent test sets. The results for our architectures outperformed the second and third-placed winners and achieved comparable results to the first-placed winner. These models demonstrated a delicate balance between efficiency and accuracy, holding their ground against larger models in performance metrics while operating on up to 98% less number of parameters and showcasing their potential for real-time application in resource-limited environments. © 2024 The Authors

关键词： Dermatology

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An Experimental Study of Keypoint Descriptor Fusion

An Experimental Study of Keypoint Descriptor Fusion

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Pan, Yaling He, Li Guan, Yisheng Zhang, Hong Guangzhou China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Shenzhen China

ISBN: (纸本)9781665481090

Local feature descriptors play a crucial role in computer vision problems, especially robot motion. Existing descriptors are highly accurate, but their performance de-pends on the influence of distracting factors, such as illumi-nation and viewpoint. There is room for further improvement of these descriptors. In this paper, we provide an in-depth analysis of several exciting features of the descriptor fusion model (DFM) we have proposed in our recent work, which uses an autoencoder to combine descriptors and exploit their respective advantages. With this DFM framework, we fur-ther validate that fused descriptors can retain advantageous properties and that our DFM is a generally applicable method with respect to various component descriptors. Specifically, we evaluate multiple combinations of hand-crafted and CNN descriptors concerning their performance on a benchmark dataset with illumination and viewpoint changes to obtain comprehensive experimental results. The results show that the fused descriptors have better matching accuracy than their component descriptors. © 2022 IEEE.

关键词： Benchmarking

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A Lightweight Blockchain Framework for Visual Homing and Navigation Robots 1

引用

6th EAI International Conference on robotics and Networks, EAI ROSENET 2022

作者： Rahouti, Mohamed Lyons, Damian M. Santana, Lesther Fordham University New YorkNY United States Fordham University The BronxNY United States Robotics and Computer Vision Laboratory The BronxNY United States

ISBN: (数字)9783031338267

ISBN: (纸本)9783031338250

Visual homing is a lightweight approach to robot visual navigation. Based upon stored visual information of a home location, the navigation back to this location can be accomplished from any other location in which this location is visible by comparing home to the current image. However, a key challenge of visual homing is that the target home location must be within the robot’s field of view (FOV) to start homing. Therefore, this chapter addresses such a challenge by integrating blockchain technology into the visual homing navigation system. Based on the decentralized feature of blockchain, the proposed solution enables visual homing robots to share their visual homing information and synchronously access the stored data (visual homing information) in the decentralized ledger to establish the navigation path. The navigation path represents a per-robot sequence of views stored in the ledger. If the home location is not in the FOV, the proposed solution permits a robot to find another robot that can see the home location and travel toward that desired location. The evaluation results demonstrate the efficiency of the proposed framework in terms of end-to-end latency, throughput, and scalability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Human-Inspired Anticipatory Motion Planning for Robotic Manipulators in Contact Tasks

Human-Inspired Anticipatory Motion Planning for Robotic Mani...

引用

IEEE International Conference on robotics and Biomimetics

作者： Malak Slim Naseem Daher Imad H. Elhajj Electrical and Computer Engineering Department Vision and Robotics Lab American University of Beirut Lebanon

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this work, we present a novel solution aimed at improving robotic manipulators' performance in contact tasks. Inspired by the human motor control system, which relies on a feedforward mechanism to anticipate and plan movements based on the physical properties of the target environment, our approach plans the robot's motion during the reaching phase, prior to contact. To validate our approach, we conducted experiments using the KUKA youBot arm in two distinct environments, represented by soft and hard materials. Results showed that the robot exhibited compliant behavior, with an average reduction of 71% in overshoot, 60% in rise-time, and 68% steady-state error of the force control response during contact.

关键词： Robot motion Motor drives computer vision Estimation Manipulators Planning Steady-state Force control Feedforward systems Robots

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An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images

SSRN

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SSRN 2024年

作者： Abramova, Valeriia Oliver, Arnau Salvi, Joaquim Terceño, Mikel Silva, Yolanda Lladó, Xavier Computer Vision and Robotics Group University of Girona Catalonia Spain Catalonia Girona Spain

Background and Objectives: Hematoma expansion (HE) occurs in 20% of patients with hemorrhagic stroke within 24 hours of onset and it is associated with a poorer patient outcome. From a clinical point of view, predicting the HE from the initial patient computed tomography (CT) image would be very useful, not only to be able to act faster but to improve therapeutic decisions. Recently, some works have started to study the prediction of the HE as a classification problem to point out if the hematoma will grow or not. However, there are not many works aiming to predict the final extent and appearance of the ***: In this work, we propose an end-to-end deep learning framework for predicting the final hematoma expansion and its corresponding lesion mask. We also explore the problem of having limited data and propose to augment the available dataset with the generation of synthetic images. The proposed approach is evaluated in different scenarios such as training using only the original CT images, using standard data augmentation techniques and using the image synthesis. We perform our experiments in a leave-one-out manner on an in-house dataset consisting of 27 hemorrhagic stroke patients with HE, where longitudinal CT images together with manual lesion annotations were ***: The obtained results showed a promising HE prediction, improving the performance when incorporating the use of synthetic images into the model. Our best model obtained a mean Dice score of the HE growth area of 0.506 and an average prediction error of hematoma volume of -3.44 *** The proposed approach showed a great potential for robust prediction of HE, highlighting also the importance of further investigations in this research line that could improve the management of hemorrhagic stroke patients in clinical practice. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Motion Planning of Quadruped Robot Using Potential Field

Motion Planning of Quadruped Robot Using Potential Field

引用

2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Voeurn, Yong Ann Raza, Muhammad Ilyas Innopolis University Institute of Robotics and Computer Vision Innopolis Russia

ISBN: (纸本)9781665424066

In motion planning, there are two main emerged directions which are inverse kinematics and control. In this paper, we present the derivation of motion control for a quadruped robot. We proposed inverse kinematics for obstacle avoidance trajectory planning using potential fields. The proposed inverse kinematics is capable of solving a wide range of complex movement skills to quadruped and provides robustness to the singularity configuration. In the control part, we derived LQR control of linearized inverse dynamics with constraints. The proposed algorithm is tested in various experiment cases in both fixed and free moving of center mass(CoM). © 2021 IEEE.

关键词： Inverse kinematics

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