Parking Assistance Application using IoT Devices and Machine Learning on the Edge

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dc.contributor.authorAmangeldina, Aruzhan
dc.contributor.authorTuleshov, Zhanabek
dc.contributor.authorBaimukhanov, Batyrkhan
dc.contributor.authorSaduakhas, Ruana
dc.date.accessioned2024-09-13T09:42:47Z
dc.date.available2024-09-13T09:42:47Z
dc.date.issued2024-04-20
dc.description.abstractTraditional parking management systems are inefficient in monitoring large scaled parking slot. This study addresses the growing issue of urban parking inefficiency, where drivers waste significant time searching for vacant spots. This problem is tackled by deploying edge devices in the parking lot, equipped with compact convolutional neural networks for real-time analysis. To evaluate performance, three models (mAlexNet224, mAlexNet32, and Mohan's model) were chosen from other research or adapted for this task. These models were then tested in a simulated parking lot environment on OpenMV or Raspberry Pi depending on the model's input size. While mAlexNet224 on Raspberry Pi achieved the best accuracy among the tested models, other lightweight models running on OpenMV also delivered satisfactory results, exceeding 90\% in accuracy. Based on the results of the study, mAlexNet32 and Mohan's model on OpenMV's camera appear well-suited for real-world deployment and the affordability of OpenMV devices further strengthens this case.en_US
dc.identifier.citationAmangeldina A., Baimukhanov B., Saduakhas R., and Tuleshov Z. (2024) Parking Assistance Application using IoT Devices and Machine Learning on the Edge. Nazarbayev University School of Engineering and Digital Sciencesen_US
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/8261
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.subjectParkingen_US
dc.subjectModel comparisonen_US
dc.subjectWeather conditionsen_US
dc.subjectOpenMVen_US
dc.subjectRaspberryPIen_US
dc.subjectLow-resource modelsen_US
dc.subjectType of access: Restricteden_US
dc.titleParking Assistance Application using IoT Devices and Machine Learning on the Edgeen_US
dc.typeBachelor's thesisen_US

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