Smart Car Rental Parking Monitoring: Pickup or Return Using AI and IoT
Bibliographic record
Abstract
Car rentals deal huge number of vehicle pickups and returns daily. When a customer picks or returns a vehicle, a manual handover happens with the car rental assistant in the parking zone. In this paper, we propose a smart car rental monitoring and parking management with automatic entry and handover process. We developed a scalable vehicle classification and identification system using deep learning and IoT. The system processed live images of vehicles entering or exiting from the parking area using images captured by an IoT-based camera. Few pretrained deep learning models such as ResNet152, GoogLeNet, and two custom CNNs were implemented and trained with a dataset of 8000 car images. Among them, ResNet152 achieved the highest validation accuracy of $\mathbf{8 8. 1 0 \%}$ in vehicle identification. We also conducted real-time vehicle identification and classification by finetuning the ResNet152. A dashboard was created for online vehicle identification from the live feed and monitoring vehicle entries or log into the system. Incremental training expanded the system to classify 202 vehicle types, and real-world evaluation confirmed its reliability. RSA encryption was used for secure data transmission and protection. This project shows potential for practical parking management by combining advanced machine learning models and secure data handling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".