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Experiments with Neural Net Object Detection System YOLO on Small Training Datasets for intelligent Robotics 1

作者： Kulik, S.D. Shtanko, A.N. National Research Nuclear University MEPHI Kashirskoe Shosse 31 Moscow115409 Russia

ISBN: (数字)9783030334918

ISBN: (纸本)9783030334901;9783030334932

In this paper we’ve conducted multiple experiments with modern object detection system YOLO. Object detection systems are fundamental to many robotics tasks. Recognition algorithms involving object detection are often part of various intelligence systems for robots. Training object detection systems usually requires waste amounts of training data which can be expensive and time-consuming. In this paper we’ve conducted several experiments with YOLO on small training datasets investigating YOLO’s capacity to train on small number of examples. We measured accuracy metrics for object detector depending on the size of training dataset, compared training process of full and smaller versions of YOLO and their speed. Gathered information will be used for creating visual factographic intelligence system for robots. YOLO (You Only Look Once) is a special intelligent technology for computer vision techniques. Our results are useful for industry professionals and students from a broad range of disciplines related to robotics, intelligent technologies and other fields. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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A WSM-based Comparative Study of vision Tracking Methodologies

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INTERNATIONAL JOURNAL OF ADVANCED computer SCIENCE AND APPLICATIONS 2021年第8期12卷 87-98页

作者： Bouraya, Sara Belangour, Abdessamad Hassan II Univ Lab Informat Technol & Modeling Fac Sci Ben Msik Casablanca Morocco

vision tracking is a key component of a video sequence. It is the process of locating single or multiple moving objects over time using one or many cameras. The latter's function consists of detecting, categorizing, and tracking. The development of the trustworthy solution for video sequence analysis opens up new horizons for a variety of applications, including intelligent transportation systems, biomedical, agriculture, human-machine interaction, augmented reality, video surveillance, robots, and many crucial research areas. To make efficient models, there are challenges in video observation to deal with, such as problems with the environment, light variation, pose variation, motion blur, clutter, occlusion, and so on. In this paper, we present several techniques that addressed the issues of detecting and tracking multiple targets on video sequences. The proposed comparative study relied on different methodologies. This paper's purpose is to list various approaches, classify them, and compare them, using the Weighted Scoring Model (WSM) comparison method. This includes studying these algorithms, selecting relevant comparison criteria, assigning weights for each criterion, and lastly computing scores. The obtained results of this study will reveal the strong and weak points of each algorithm mentioned and discussed.

关键词： Multiple object tracking object detection WSM method computer vision video analysis

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Autonomous oil spill and pollution detection for large-scale conservation in marine eco-cyber-physical systems

Autonomous oil spill and pollution detection for large-scale...

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International Conference of Signal Processing and intelligent Systems (ICSPIS)

作者： Asma Bahrani Babak Majidi Mohammad Eshghi Khatam University Tehran Iran Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781665409391

In recent years, advancement of the industry and increased human activities created significant pollution in the marine environment and coastal regions of the Persian Gulf. These pollutions cause various diseases and serious damages to the human health and animal species. Early identification of various pollutions helps the coastal management to organize their resources and rapidly respond to the problems. Due to the large scale of the coastal regions, manual investigation of the pollutions is a very time-consuming task. Unmanned robots can be used as autonomous agents for rapid large-scale detection and classification of pollutions in the coastal regions. In this paper, an artificial intelligence-based vision system for autonomous marine pollution detection is proposed. A combination of computer vision and machine learning methods are used for autonomous detection of various pollutions in the coastal and marine environment. In this study, 3000 images of Persian Gulf coastal pollutions is collected and used for training an artificial intelligence system for coastal conservation. The experimental results shows that the proposed framework has a 98% accuracy for identifying and classifying coastal and marine pollutions. The proposed system can be used as the vision system of an autonomous coastal conservation robot and increase the speed of coastal conservation and management significantly.

关键词： Pollution Machine learning algorithms Marine pollution Environmentally friendly manufacturing techniques Machine vision Sea measurements Signal processing algorithms

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Towards vision-Based Impedance Control for the Contact Inspection of Unknown Generically-Shaped Surfaces with a Fully-Actuated UAV

Towards Vision-Based Impedance Control for the Contact Inspe...

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2020 IEEE/RSJ International Conference on intelligent robots and Systems (IROS)

作者： Ramy Rashad Davide Bicego Ran Jiao Santiago Sanchez-Escalonilla Stefano Stramigioli Robotics and Mechatronics group University of Twente Enschede The Netherlands School of Mechanical Engineering and Automation Beihang University Beijing China Robotics and Mechatronics group University of Twente and ITMO University Saint Petersburg Russia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

The integration of computer vision techniques for the accomplishment of autonomous interaction tasks represents a challenging research direction in the context of aerial robotics. In this paper, we consider the problem of contact-based inspection of a textured target of unknown geometry and pose. Exploiting state of the art techniques in computer graphics, tuned and improved for the task at hand, we designed a framework for the projection of a desired trajectory for the robot end-effector on a generically-shaped surface to be inspected. Combining these results with previous work on energy-based interaction control, we are laying the basis of what we call vision-based impedance control paradigm. To demonstrate the feasibility and the effectiveness of our methodology, we present the results of both realistic ROS/Gazebo simulations and preliminary experiments with a fully-actuated hexarotor interacting with heterogeneous curved surfaces whose geometric description is not available a priori, provided that enough visual features on the target are naturally or artificially available to allow the integration of localization and mapping algorithms.

关键词： Surface impedance Inspection End effectors Trajectory Surface texture Impedance Task analysis

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高能效视觉SLAM硬件加速器设计

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集成电路与嵌入式系统 2024年第11期24卷 51-59页

作者：齐修远刘野郝爽周军电子科技大学信息与通信工程学院成都611731

随着计算机视觉技术的不断迭代和发展,以计算机视觉技术为核心的智能应用和设备逐渐在人们的日常生活和工作中扮演越来越重要的角色。其中,基于视觉的同步定位与建图技术(Simultaneous Localization and Mapping,SLAM)在机器人、无人机... 详细信息

随着计算机视觉技术的不断迭代和发展,以计算机视觉技术为核心的智能应用和设备逐渐在人们的日常生活和工作中扮演越来越重要的角色。其中,基于视觉的同步定位与建图技术(Simultaneous Localization and Mapping,SLAM)在机器人、无人机、自动驾驶等领域中被广泛应用,上述领域需要视觉SLAM技术为其提供精准的定位信息,以实现其精确建图和自主导航功能。然而,由于视觉SLAM算法本身的特性,计算量极大,数据依赖性极高,导致其在传统的硬件平台(CPU或GPU)上运行时,难以满足前述边缘端应用场景对实时性和低功耗的需求,成为限制视觉SLAM技术被广泛应用的关键因素。为了解决这一问题,本文基于算法与硬件协同设计的优化策略,针对ORB特征提取和匹配算法提出了一种面向视觉SLAM的高能效专用加速器,通过多种硬件设计技术提高计算性能和能效,包括基于数据依赖关系解耦的多层次并行计算技术、基于多尺寸存储桶的数据存储技术和像素级对称-轻量化描述子生成和方向计算策略。提出的视觉SLAM加速器在Xilinx ZCU104上进行了测试和验证。与ORB SLAM2的算法精度对比,本加速器的精度在5%以内,帧率提升至108 fps,与同期其他硬件加速器相比,查找表使用降低了32.7%,FF使用降低了41.17%,同时帧率提升了1.4倍和0.74倍。

关键词：视觉SLAM 领域专用芯片硬件加速器机器人

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3D vision for Precision Dairy Farming 6th

3D Vision for Precision Dairy Farming

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6th International-Federation-of-Automatic-Control (IFAC) Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： O'Mahony, Niall Campbell, Sean Carvalho, Anderson Krpalkova, Lenka Riordan, Daniel Walsh, Joseph Inst Technol Tralee IMaR Res Ctr Tralee Co Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automation and Robotics in Agriculture Sensing and Automation in Animal Fanning

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Indoor Scene Recognition based on Weighted Voting Schemes

Indoor Scene Recognition based on Weighted Voting Schemes

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European Conference on Mobile robots (ECMR)

作者： Hernandez, Alejandra C. Gomez, Clara Derner, Erik Barber, Ramon Carlos III Univ Madrid Dept Syst Engn & Automat Robot Lab Madrid Spain Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Control Engn Prague Czech Republic

ISBN: (纸本)9781728136059

Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. "office", "classroom" or "kitchen") can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.

关键词： Scene recognition multi-classification scene understanding mobile robots genetic algorithms

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Smart gas sensor arrays powered by artificial intelligence

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Journal of Semiconductors 2019年第11期40卷 3-12页

作者： Zhesi Chen Zhuo Chen Zhilong Song Wenhao Ye Zhiyong Fan HKUST-Shenzhen Research Institute Shenzhen 518057China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology(HKUST)Hong KongChina

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas *** intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and *** is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home *** this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive *** also discuss the key limitations of gas sensor units and their possible ***,we present the outlook of GSAs over the horizon of smart homes and cities.

关键词： mobile robots gas sensor array electronic nose artificial intelligence environmental monitoring disease diagnosis

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3D vision for Precision Dairy Farming

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IFAC-PapersOnLine 2019年第30期52卷 312-317页

作者： Niall O’ Mahony Sean Campbell Anderson Carvalho Lenka Krpalkova Daniel Riordan Joseph Walsh IMaR Research Centre Institute of Technology Tralee Tralee Co. Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals. This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning.

关键词： Automation Robotics in Agriculture Sensing Automation in Animal Farming

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Artificial intelligence for robotics : build intelligent robots that perform human tasks using AI...

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2018年

作者： Govers Francis X.

来源：内蒙古大学图书馆图书评论

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