IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2542-d1052541.html
   My bibliography  Save this article

Application of Target Detection Method Based on Convolutional Neural Network in Sustainable Outdoor Education

Author

Listed:
  • Xiaoming Yang

    (Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
    College of Physical Education, East China University of Technology, Nanchang 330013, China)

  • Shamsulariffin Samsudin

    (Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Yuxuan Wang

    (Sports Institute, Nangchang Jiao Tong Institute, Nanchang 330100, China)

  • Yubin Yuan

    (Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Tengku Fadilah Tengku Kamalden

    (Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Sam Shor Nahar bin Yaakob

    (Department of Nature Parks and Recreation, Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Malaysia)

Abstract

In order to realize the intelligence of underwater robots, this exploration proposes a submersible vision system based on neurorobotics to obtain the target information in underwater camera data. This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine the target information in underwater camera data. First, the underwater functions of the manned submersible are analyzed and mined to obtain the specific objects and features of the underwater camera information. Next, the dataset of the specific underwater target image is further constructed. The acquisition system of underwater camera information of manned submersibles is designed through the Single Shot-MultiBox Detector algorithm of deep learning. Furthermore, CNN is adopted to classify the underwater target images, which realizes the intelligent detection and classification of underwater targets. Finally, the model’s performance is tested through experiments, and the following conclusions are obtained. The model can recognize underwater organisms’ local, global, and visual features. Different recognition methods have certain advantages in accuracy, speed, and other aspects. The design here integrates deep learning technology and computer vision technology and applies it to the underwater field, realizing the association of the identified biological information with the geographic information and marine information. This is of great significance to realize the multi-information fusion of manned submersibles and the intelligent field of outdoor education. The contribution of this exploration is to provide a reasonable direction for the intelligent development of outdoor diving education.

Suggested Citation

  • Xiaoming Yang & Shamsulariffin Samsudin & Yuxuan Wang & Yubin Yuan & Tengku Fadilah Tengku Kamalden & Sam Shor Nahar bin Yaakob, 2023. "Application of Target Detection Method Based on Convolutional Neural Network in Sustainable Outdoor Education," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2542-:d:1052541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2542/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2542/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    2. Pin-Wei Chen & Nathan A. Baune & Igor Zwir & Jiayu Wang & Victoria Swamidass & Alex W.K. Wong, 2021. "Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    3. Guofeng Ma & Xuhui Pan, 2021. "Research on a Visual Comfort Model Based on Individual Preference in China through Machine Learning Algorithm," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vincent Y. Chen & Day-Jye Lu & Yu-San Han, 2024. "Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan," Sustainability, MDPI, vol. 16(1), pages 1-20, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davor Stjelja & Juha Jokisalo & Risto Kosonen, 2022. "Scalable Room Occupancy Prediction with Deep Transfer Learning Using Indoor Climate Sensor," Energies, MDPI, vol. 15(6), pages 1-21, March.
    2. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.
    3. Bigerna, Simona & D'Errico, Maria Chiara & Polinori, Paolo, 2022. "Understanding the green-growth: which pathways cities undertake in their climate programs," MPRA Paper 114156, University Library of Munich, Germany.
    4. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    5. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    6. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Jierui Dong & Nigel Goodman & Priyadarsini Rajagopalan, 2023. "A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools," IJERPH, MDPI, vol. 20(15), pages 1-18, July.
    8. Zedong Jiao & Xiuli Du & Zhansheng Liu & Liang Liu & Zhe Sun & Guoliang Shi & Ruirui Liu, 2023. "A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    9. Pedro Fernández de Córdoba & Frank Florez Montes & Miguel E. Iglesias Martínez & Jose Guerra Carmenate & Romeo Selvas & John Taborda, 2023. "Design of an Algorithm for Modeling Multiple Thermal Zones Using a Lumped-Parameter Model," Energies, MDPI, vol. 16(5), pages 1-22, February.
    10. Woon, Kok Sin & Phuang, Zhen Xin & Taler, Jan & Varbanov, Petar Sabev & Chong, Cheng Tung & Klemeš, Jiří Jaromír & Lee, Chew Tin, 2023. "Recent advances in urban green energy development towards carbon emissions neutrality," Energy, Elsevier, vol. 267(C).
    11. Andrzej Pacana & Karolina Czerwińska & Grzegorz Ostasz, 2023. "Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity," Energies, MDPI, vol. 16(8), pages 1-26, April.
    12. Axelle Gelineau & Anaick Perrochon & Louise Robin & Jean-Christophe Daviet & Stéphane Mandigout, 2022. "Measured and Perceived Effects of Upper Limb Home-Based Exergaming Interventions on Activity after Stroke: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    13. Jiao Xue & Yige Fan & Zhanxun Dong & Xiao Hu & Jiatong Yue, 2022. "Improving Visual Comfort and Health through the Design of a Local Shading Device," IJERPH, MDPI, vol. 19(7), pages 1-20, April.
    14. Monika Górska & Marta Daroń, 2021. "Importance of Machine Modernization in Energy Efficiency Management of Manufacturing Companies," Energies, MDPI, vol. 14(24), pages 1-19, December.
    15. Mohammed Lami & Faris Al-naemi & Hameed Alrashidi & Walid Issa, 2022. "Quantifying of Vision through Polymer Dispersed Liquid Crystal Double-Glazed Window," Energies, MDPI, vol. 15(9), pages 1-23, April.
    16. Qiang, Guofeng & Tang, Shu & Hao, Jianli & Di Sarno, Luigi & Wu, Guangdong & Ren, Shaoxing, 2023. "Building automation systems for energy and comfort management in green buildings: A critical review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2542-:d:1052541. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.