Conference paper Open Access

DroNet: Efficient convolutional neural network detector for real-time UAV applications

Christos Kyrkou; George Plastiras; Theocharis Theocharides; Stylianos I. Venieris; Christos-Savvas Bouganis


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    "description": "<p>Deep learning (DL) has gathered significant interest recently as an Artificial Intelligence (AI) paradigm, with success in a wide range of applications such as image and speech recognition, autonomous systems, self-driving cars, cyber-physical systems, and many more. Among the most promising systems that can utilize deep learning are Unmanned Aerial Vehicles (UAVs) which are becoming an attractive solution for a wide range of applications. In particular, Road Traffic Monitoring (RTM), and Emergency Response (ER) systems constitute a domain where the use of UAVs is receiving significant interest. Under the above deployments, UAVs are responsible for searching, collecting and sending, in real time, vehicle information either for traffic regulation purposes or to aid search and rescue in emergency response.</p>", 
    "license": {
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    "title": "DroNet: Efficient convolutional neural network detector for real-time UAV applications", 
    "notes": "\u00a9 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works.\n\nC. Kyrkou, G. Plastiras, T. Theocharides, S. I. Venieris and C. S. Bouganis, \"DroNet: Efficient convolutional neural network detector for real-time UAV applications,\" 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 2018, pp. 967-972.\ndoi: 10.23919/DATE.2018.8342149\n\n\nhttps://www.ieee.org/publications_standards/publications/rights/rights_policies.html\n\nNVIDIA Corporation has supported this research with the donation of the Titan Xp GPU", 
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    "keywords": [
      "Detectors, Convolutional neural networks, Real-time systems, Training, Computer architecture, Machine learning, Graphics processing units"
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    "publication_date": "2018-03-16", 
    "creators": [
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        "affiliation": "KIOS Center of Excellence, University of Cyprus", 
        "name": "Christos Kyrkou"
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      {
        "affiliation": "KIOS Center of Excellence, University of Cyprus", 
        "name": "George Plastiras"
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        "affiliation": "KIOS Center of Excellence,  Department of Electrical and Computer Engineering, University of Cyprus", 
        "name": "Theocharis Theocharides"
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      {
        "affiliation": "Department of Electrical and Electronic Engineering, Imperial College London", 
        "name": "Stylianos I. Venieris"
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        "affiliation": "Department of Electrical and Electronic Engineering, Imperial College London", 
        "name": "Christos-Savvas Bouganis"
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