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Toyota-Corolla-Car-Parts

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ARabeitak Car Parts Object Detection Dataset

Dataset Summary

The ARabeitak Car Parts Object Detection Dataset is a small, task-oriented image dataset of car engine compartments captured to train and evaluate object detection models.

Images were collected as part of the graduation project:

“ARabeitak: Markerless Augmented Reality Platform for User Support – ‘ARabeitak’ Car Maintenance App as a Case Study”
Judy Wagdy, Laila Hany, Rana Raafat, Steven Albert
Supervised by Dr. Essam Eliwa, Eng. Nada Ayman
Submitted June 2023, in partial fulfillment of the requirements for the degree of BSc in Computer Science.

The dataset focuses on common under-the-hood components that a non-expert driver may need to locate during basic car maintenance tasks. It was used to train object detection models powering the ARabeitak mobile application, which overlays AR guidance directly on the car’s engine bay.

Dataset Description

Use Cases

Training and evaluating object detection models for:
- Interactive AR guidance for car maintenance.
- Intelligent car manuals / remote support.
- Educational applications for basic automotive literacy.

Data Modality

Images: RGB photographs of car engine bays and related parts.

Structure

Images are organized into 7 folders by car part / view.
7 folders correspond to the labeled object classes listed below.

Classes

The dataset is categorized into the following 7 object classes.
The numbers below indicate the current count of object images per class:

Class Label	Count
Air Filter	206
Battery	269
Coolant Reservoir	296
Engine	212
Engine Cover	187
Reservoir Cap	11
Windshield Fluid	171

Data Instances

Each instance includes:

Image
- Under-the-hood photo taken from a user’s point of view.
- Real-world conditions: dust, shadows, varying illumination, etc.

Collection Process

Source

Photos captured from a Toyota Corolla 2015 engine bay.
Shot with consumer-grade (Samsung Galaxy S20+) mobile device in natural conditions (no studio setup).

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