An Efficient UAV-based Artificial Intelligence Framework for Real-Time Visual Tasks

• URL: http://arxiv.org/abs/2004.06154v1
• Date: Mon, 13 Apr 2020 18:53:12 GMT
• Title: An Efficient UAV-based Artificial Intelligence Framework for Real-Time Visual Tasks
• Authors: Enkhtogtokh Togootogtokh, Christian Micheloni, Gian Luca Foresti, Niki Martinel
• Abstract summary: We introduce a multi-layer AI (MLAI) framework to allow easy integration of ad-hoc visual-based AI applications. To show its features and its advantages, we implemented and evaluated different modern visual-based deep learning models for object detection, target tracking and target handover.
• Score: 33.489573797811474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern Unmanned Aerial Vehicles equipped with state of the art artificial intelligence (AI) technologies are opening to a wide plethora of novel and interesting applications. While this field received a strong impact from the recent AI breakthroughs, most of the provided solutions either entirely rely on commercial software or provide a weak integration interface which denies the development of additional techniques. This leads us to propose a novel and efficient framework for the UAV-AI joint technology. Intelligent UAV systems encounter complex challenges to be tackled without human control. One of these complex challenges is to be able to carry out computer vision tasks in real-time use cases. In this paper we focus on this challenge and introduce a multi-layer AI (MLAI) framework to allow easy integration of ad-hoc visual-based AI applications. To show its features and its advantages, we implemented and evaluated different modern visual-based deep learning models for object detection, target tracking and target handover.

Related papers

• Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
  The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
  arXiv  Detail & Related papers  (2024-10-28T13:03:09Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• Operating System And Artificial Intelligence: A Systematic Review [17.256378758253437]
  We explore how AI-driven tools enhance OS performance, security, and efficiency, while OS advancements facilitate more sophisticated AI applications. We analyze various AI techniques employed to optimize OS functionalities, including memory management, process scheduling, and intrusion detection. We explore the promising prospects of Intelligent OSes, considering not only how innovative OS architectures will pave the way for groundbreaking opportunities but also how AI will significantly contribute to advancing these next-generation OSs.
  arXiv  Detail & Related papers  (2024-07-19T05:29:34Z)
• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes. We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
  Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents. We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
  arXiv  Detail & Related papers  (2023-04-10T03:55:35Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• FedVision: An Online Visual Object Detection Platform Powered by Federated Learning [28.644610569780713]
  FedVision is a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.
  arXiv  Detail & Related papers  (2020-01-17T09:02:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.