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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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IEEE International conference on Power, intelligent Computing and Systems (ICPICS)

作者： Herun Zhong School of Arts and Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. this article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. the discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks.

关键词： Deep learning computer vision Visualization Image recognition Benchmark testing Visual systems Classification algorithms Object recognition Convolutional neural networks Videos

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Reexamining Indoor Mobile robots from a Cognitive Perspective

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

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Robotics and computer vision (ICRCV), International conference on

作者： Fei Song Jinyu Li Fengzhen Tang Yandong Tang Bailu Si State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

the field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. this article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. these phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques.

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Feature detection and map building using ranging sensors

Feature detection and map building using ranging sensors

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International conference on intelligent Transportation

作者： M. Kulich P. Stepan L. Preucil The Gerstner Laboratory for Intelligent Decision Making and Control Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

this paper describes two methods for feature extraction. the goal is to interpret the data obtained from sonar measurements of the robot's environment as geometric map primitives. the extracted primitives can be used to update the robot position and the global map. this is the process how the robot can explore an unknown environment. the first algorithm gathers sonar measurements into sensor-based map and then using computer-vision techniques and morphological operations extracts geometric description of detected obstacles. the second method is started whenever the border tracking algorithm has been activated. this method uses raw sonar data as the input while obstacle border is robustly detected by the border tracking process. the algorithms have been verified with an experimental mobile robot at the Czech Technical University.

关键词： computer vision Robot sensing systems Sonar measurements Mobile robots Sonar detection Sensor fusion intelligent sensors Data mining Robustness Laboratories

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Integration of a fuzzy guidance-control system for a command interceptor against hypersonic target

Integration of a fuzzy guidance-control system for a command...

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Annual conference of Industrial Electronics Society

作者： Y.Z. Elhalwagy M. Tarbouchi Department of Electrical and Computer Engineering Royal Military College of Canada Kingston ONT Canada

Extensive research efforts have been made to design or improve flight guidance and control systems. With the phenomenal growth in soft computing techniques, there is a growing interest to apply these techniques to missile flight guidance and control systems. this paper investigates the application of fuzzy logic for the development of fuzzy guidance laws for command-to-line-of-sight skid-to-turn (CLOS-STT) missile. the fuzzy logic guidance scheme based on the well known three-point guidance law is discussed. Preliminary studies have shown the advantages of using anti-air tactical missiles against hypersonic ballistic targets. A complete six-degrees-of-freedom (6-DOF)-flight model for an anti-air command guidance system is developed. Simulation results for this three-dimensional missile-target interception process are given to show the effectiveness of the proposed fuzzy guidance scheme.

关键词： Fuzzy systems Missiles Control systems Fuzzy logic Aerodynamics intelligent control Military computing Educational institutions Military aircraft computer vision

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Development of computer vision Based Aluminium Scrap Sorting Using Real Time Metal Scrap Dataset

Development of Computer Vision Based Aluminium Scrap Sorting...

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Sustainable Expert Systems (ICSES), International conference on

作者： Adhithya S J. Kathirvelan School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

the aluminium manufacturing industries face numerous challenges when it comes to sorting metal scraps, particularly when scraps are small in size. Traditional methods like flotation and gravity separation are inefficient, energy-intensive, and often fail when dealing with size variations. Recent advancements in computer vision and deep learning algorithms, including intelligent robotic systems, offer promising solutions. However, challenges such as accurate detection in complex environments, varying lighting conditions, and overlapping objects still persist. Newer techniques like YOLO (You Only Look Once) for real-time object detection, and Transfer Learning to improve model accuracy with smaller datasets, have shown potential. this research aims to overcome these challenges by developing an AI-powered, computer vision-based sorting system that can identify and classify aluminium scraps by color and shape at high speed. the authors developed a custom dataset and trained models using CNN architectures such as DenseNet-161, ResNet-152, and VGG 19. By employing ensemble techniques, this research achieved an 75% of accuracy.

关键词： Deep learning YOLO computer vision Accuracy Service robots Computational modeling Aluminum Real-time systems Recycling Sorting

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Automated change impact analysis for agent systems

Automated change impact analysis for agent systems

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International conference on Software Maintenance (ICSM)

作者： Hoa Khanh Dam Aditya Ghose School of Computer Science and Software Engineering University of Wollongong NSW Australia

intelligent agent technology has evolved rapidly over the past few years along with the growing number of agent systems in various domains. Although a substantial amount of work in agent-oriented software engineering has provided methodologies for analysing, designing and implementing agent-based systems, recent studies have highlighted that there has been very little work on maintenance and evolution of agent-based systems. A critical issue in software maintenance and evolution is change impact analysis: determining the potential consequences of a proposed change. there has been a proliferation of techniques proposed to support change impact analysis of procedural or object-oriented systems, but to the best of our knowledge, no such an effort has been made for agent-based software. In this paper, we fill this gap by proposing a framework to support change impact analysis for agent systems. At the core of our framework is the taxonomy of atomic changes which can precisely capture semantic differences between versions of an agent system. We also present a change impact model in the form of an intra-agent dependency graph that represents various dependencies within an agent system. An algorithm to compute the set of entities impacted by a change is also presented. the proposed techniques have been implemented in AgentCIA, a change impact analysis plugin for Jason, one of the most well-known agent programming platforms.

关键词： robots Context Taxonomy Libraries computer architecture Software maintenance

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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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Particle filter localization and real time obstacle avoidance control based on 3D perception for wheelchair mobile robot

Particle filter localization and real time obstacle avoidanc...

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International IEEE conference on intelligent Systems, IS

作者： Emna Baklouti Nader Ben Amor Mohamed Jallouli Ecole Nationale d'Ingénieur de Sfax Université de Sfax Tunisie Computer & Embedded Systems Laboratory (CES)

Obstacle detection and localization behaviours have shown to work robustly in 2D perceived environment. With the progress of proximity sensors, precisely the emergence of 3D vision techniques, it would be interesting to examine these motion controllers whether for 3D perceived environment. 2D basic algorithms for obstacle avoidance or robot location, needs reformulation in order to process data from 3D perception devices. In this work, we introduce a 3D obstacle detection controller, combined with particle filter localization. the obstacle detection control described in this paper address 3D obstacles with different shapes and the particle filter uses 3D environment data to correct the wheelchair localization based on his kinematics model.

关键词： three-dimensional displays Wheelchairs robots Two dimensional displays Kinematics Collision avoidance Sensors

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Towards Latency Efficient DRL Inference: Improving UAV Obstacle Avoidance at the Edge through Model Compression

Towards Latency Efficient DRL Inference: Improving UAV Obsta...

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International conference on intelligent Transportation

作者： Patrick McEnroe Shen Wang Madhusanka Liyanage School of Computer Science University College Dublin Dublin Ireland

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

It is vital that autonomous Unmanned Aerial Vehicles (UAVs) are able to avoid obstacles effectively. When avoiding such obstacles it is important that movement decision are made fast (i.e. inference latency is low) so that crashes are avoided. When deep reinforcement learning (DRL) is being leveraged as the method of obstacle avoidance one way of reducing this inference latency is to deploy the DRL model at the edge (e.g., on-UAV). However, even if the DRL model is small enough to be deployed on-UAV, the inference latency can be too high. there is a lack of research that examines reducing DRL inference time of UAVs when avoiding obstacles. thus, this paper examines various model compression techniques to improve the inference speed of such DRL models deployed at the edge. We propose an approach that combines these model compression techniques and apply it to a well performing Dueling Double Deep Q-Network (D3QN) baseline model. On the Nvidia Jetson Orin Nano and Nvidia Jetson Nano edge devices we show that, relative to our baseline model, this combined model compression approach reduces inference latency by 38.61% and 53.18% while only reducing the success rate by 2.34% and 5% respectively. All our code is open-sourced.

关键词： Performance evaluation Codes Navigation Autonomous aerial vehicles Deep reinforcement learning Nanoscale devices Inference algorithms Collision avoidance Autonomous robots Accidents

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A review on tracking head movement in augmented reality systems 27

A review on tracking head movement in augmented reality syst...

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27th International conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Nwobodo, Onyeka J. Wereszczynski, Kamil Cyran, Krzysztof Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

this paper reviews the recent development of augmented reality (AR) head-tracking techniques. AR is an emerging technology that provides users with digital augmentation layered on the real-life environment. AR systems use tracking to maintain the point of reference and enable the device to follow the user's movements and position within the physical world. Head-tracking is a crucial component of AR that inputs and interprets the movement of the device and the user's physical location in real-time. the main challenge in head-tracking is the appearance change that results from head rotation, which necessitates high accuracy and long-range tracking in a noisy environment. Latency and errors in head-tracking can reduce the system's overall effectiveness and cause jarring or unsettling effects for the user. this paper's contribution is to shed light on the best methods for tracking and registering in augmented reality, which will guide future advancements in the field. the review covers recent work proposed to improve head-tracking performance and reduce latency, highlighting the challenges and future directions of head-tracking techniques in AR. © 2023 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)

关键词： augmented reality deep learning DOF Head tracking immersive experience pose estimation

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