Automated Vehicle parking system and unauthorised parking detector using AI based models
It is feasible to create a world in which the use of technology makes every task simpler with the right application of science and engineering. To guarantee safety and comfort, parking management must be automated just like any other industry. It has become more difficult to locate enough space for...
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| Published in: | 2023 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC) pp. 1 - 7 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
14-12-2023
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | It is feasible to create a world in which the use of technology makes every task simpler with the right application of science and engineering. To guarantee safety and comfort, parking management must be automated just like any other industry. It has become more difficult to locate enough space for car parking due to the massive upsurge in both populace and the number of automobiles. Not only does this squander our time, but it also drains our energy. That's why it's been so important to have the idea of an automatic car parking system and an unlawful parking detector. In addition, the number of illegal cars on the road has grown. Because of this, our suggested system seeks to guarantee effective management of automobiles in public spaces like educational institutes, offices, etc., in an effort to stop illegal parking and traffic. There is a pressing need for the Systems to address the aforementioned problems (SPS). To address the challenges of real-time parking management and the unknown, the authors of this publication suggest a Smart Parking System that utilises Internet of Things (IoT) and deep learning technologies. For deep learning tasks, the authors suggest using Shepard Interpolation Neural Networks (SINNs), a shallow learning construction. SINN, which is not based on a biological brain but on statistical interpolation, may be trained to reach great accuracy with very little data. Its explainability is analogous to mapping features onto hyper surfaces in the feature space. The projected system is capable of detecting multiple objects in a parking slot like a bike in a car slot, identifying faults in one or more components, and managing traffic during peak hours. It also indicates the status of parking slots in advance to end-users and accounts for parking, unauthorised parking, and real-time analysis of free and occupied slots. Less need for human input means time, money, and energy savings.We also provide a new SNUSPS dataset, using it to make estimates of the system's performance from different perspectives and to undertake evaluations of the system in parking assignment tasks. Our dataset's results demonstrate our system's potential for use in the real world.. |
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| DOI: | 10.1109/TEMSCON-ASPAC59527.2023.10531356 |