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

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 f...

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Bibliographic Details
Published in:Journal of real-time image processing 2022-02, Vol.19 (1), p.103-115
Main Authors: Martinez-Alpiste, Ignacio, Golcarenarenji, Gelayol, Wang, Qi, Alcaraz-Calero, Jose Maria
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Classification
Computer Graphics
Computer Science
Constraints
Deep learning
Embedded systems
Image Processing and Computer Vision
Machine learning
Multimedia Information Systems
Object recognition
Original Research Paper
Pattern Recognition
Portability
Power consumption
Real time
Sensors
Signal,Image and Speech Processing
Smartphones
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Summary: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.
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-021-01164-1