Skip to main content
SpringerLink
Log in

Object Detection at Edge Using TinyML Models

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

With the penetration of IoT across sectors, image classification becomes a critical issue if the computations have to be done at the edge. The evolution of low-cost devices with powerful processing for any vision-based systems leads to the next requirement of machine learning for imaging with reduced latency and reliability along with data security. Running the energy hungry computer vision techniques which need frequent memory access may not be the solution. This paper deliberates the works carried out which can be deployed at the end devices, such as mobiles, microcontrollers for image computing at edge. The approach used is by leveraging the TinyML optimized features for low latency and energy efficiency. This paper deals with sample data worked for classification on ML methods, namely, FOMO and MobilenNetSSD which are converted from the Tensorflow to lite version, using the edge Impulse platform. The results discussed are taken from the deployed TinyML model on mobile phone. The outcomes along with the accuracy are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Not application.

References

  1. Leonard JK. Image classification and object detection algorithm based on convolutional neural network. Sci Insights. 2019;31(1):85–100.

    Article  MathSciNet  Google Scholar 

  2. Zhao Z-Q, Zheng P, Shou-tao Xu, Xindong Wu. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019;30(11):3212–32.

    Article  Google Scholar 

  3. Liu X, Deng Z, Yang Y. Recent progress in semantic image segmentation. Artif Intell Rev. 2019;52:1089–106.

    Article  Google Scholar 

  4. Pete Warden’s Blog, How many images do you need to train a neural network?, https://petewarden.com/2017/12/14/how-many-images-do-you-need-to-train-a-neural-network/.

  5. Oleksii V. GPU for deep learning: benefits & drawbacks of on-premises vs cloud. In: Techinsights Mobidev. 2021.

  6. Goel A, Caleb T, Yung-Hsiang L, George KT. A survey of methods for low-power deep learning and computer vision. In: 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1–6. IEEE. 2020.

  7. Warden P, Daniel S. Tinyml: Machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers. O'Reilly Media. 2019.

  8. Simonyan K, Andrew Z. Very deep convolutional networks for large-scale image recognition. 2014. arXiv preprint arXiv:1409.1556.

  9. Alajlan NN, Ibrahim DM. TinyML: enabling of inference deep learning models on ultra-low-power IoT edge devices for AI applications. Micromachines. 2022;13(6):851.

    Article  Google Scholar 

  10. Paul M. FOMO offers ML object detection for microcontrollers. 2022.

  11. Raza W, Anas O, Francesco F, Francesco DN. Energy-efficient inference on the edge exploiting TinyML capabilities for UAVs. Drones. 2021;5(4):127.

    Article  Google Scholar 

  12. Ben D. FOMO is a TinyML neural network for real-time object detection. In:Tech Talks newsletter. 2022.

  13. Artificial Intelligence Board of America (ARTiBA). TinyML: The Future of Machine Learning. 2022.

  14. Schizas N, Karras A, Karras C, Sioutas S. TinyML for ultra-low power AI and large scale IoT deployments: a systematic review. Fut Internet. 2022;14(12):363.

    Article  Google Scholar 

Download references

Funding

No funds has been received for this work.

Author information

Authors and Affiliations

  1. Department of MCA, R.V. College of Engineering, Bengaluru, India

    Andhe Dharani, S. Anupama Kumar & Peethi N. Patil

  2. Affiliated to Visvesvaraya Technological University, Belagavi, India

    Andhe Dharani, S. Anupama Kumar & Peethi N. Patil

Authors
  1. Andhe Dharani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Anupama Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peethi N. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andhe Dharani.

Ethics declarations

Conflict of Interest

The authors of this manuscript have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dharani, A., Kumar, S.A. & Patil, P.N. Object Detection at Edge Using TinyML Models. SN COMPUT. SCI. 5, 11 (2024). https://doi.org/10.1007/s42979-023-02304-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02304-z

Keywords

Search

Navigation