Smartphone-based real-time object recognition architecture for portable and constrained systems

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Machine learning algorithms based on convolutional neural networks (CNNs) have recently been explored in a myriad of object detection applications. Nonetheless, many devices with limited computation resources and strict power consumption constraints are not suitable to run such algorithms designed for high-performance computers. Hence, a novel smartphone-based architecture intended for portable and constrained systems is designed and implemented to run CNN-based object recognition in real time and with high efficiency. The system is designed and optimised by leveraging the integration of the best of its kind from the state-of-the-art machine learning platforms including OpenCV, TensorFlow Lite, and Qualcomm Snapdragon informed by empirical testing and evaluation of each candidate framework in a comparable scenario with a high demanding neural network. The final system has been prototyped combining the strengths from these frameworks and led to a new machine learning-based object recognition execution environment embedded in a smartphone with advantageous performance compared with the previous frameworks.
Original languageEnglish
Pages (from-to)103-115
Number of pages13
JournalJournal of Real-Time Image Processing
Early online date1 Sept 2021
Publication statusPublished - 28 Feb 2022


  • machine learning
  • object recognition
  • deep learning platforms
  • CNN
  • YOLOv3
  • embedded systems


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