SRCNN-BASED IMAGE TRANSMISSION FOR AUTONOMOUS VEHICLES IN LIMITED NETWORK AREAS

    Anindya Afina Carmelya, - (2025) SRCNN-BASED IMAGE TRANSMISSION FOR AUTONOMOUS VEHICLES IN LIMITED NETWORK AREAS. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.

    [thumbnail of TA_ART_SISTEL_2104075_SK.pdf] Text
    TA_ART_SISTEL_2104075_SK.pdf

    Download (1MB)
    [thumbnail of TA_ART_SISTEL_2104075_ART.pdf] Text
    TA_ART_SISTEL_2104075_ART.pdf
    Restricted to Staf Perpustakaan

    Download (3MB)
    Official URL: http://doi.org/10.11591/ijeecs.v37.i2.pp903-912
    Item Type: Thesis (S1)
    Additional Information: https://scholar.google.com/citations?user=oeo6YkkAAAAJ&hl=id&oi=ao ID SINTA Dosen Pembimbing: Galura Muhammad Suranegara: 6703764
    Uncontrolled Keywords: Autonomous vehicles; High-resolution images; Image transmission; Network efficiency; SRCNN
    Subjects: T Technology > T Technology (General)
    Divisions: UPI Kampus Purwakarta > S1 Sistem Telekomunikasi
    Depositing User: Anindya Afina Carmelya
    Date Deposited: 10 Feb 2025 03:17
    Last Modified: 10 Feb 2025 03:17
    URI: http://repository.upi.edu/id/eprint/130499

    Actions (login required)

    View Item View Item