stevenalbert10/Toyota-Corolla-Car-Parts

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license: apache-2.0
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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.

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## 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.

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## 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 |

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## 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).

许可证：Apache-2.0
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# ARabeitak汽车零件目标检测数据集（ARabeitak Car Parts Object Detection Dataset）

## 数据集摘要
**ARabeitak汽车零件目标检测数据集（ARabeitak Car Parts Object Detection Dataset）**是一款小型、面向特定任务的汽车发动机舱图像数据集，用于训练和评估目标检测（object detection）模型。

该数据集采集自某本科毕业设计项目：
> **《ARabeitak：面向用户支持的无标记增强现实（Augmented Reality，AR）平台——以“ARabeitak”汽车维修应用为例》**
> *作者：Judy Wagdy、Laila Hany、Rana Raafat、Steven Albert*
> *指导教师：Essam Eliwa博士、Nada Ayman工程师*
> *2023年6月提交，为计算机科学学士学位论文的部分研究成果*

本数据集聚焦普通非专业驾驶员在基础汽车维修作业中可能需要查找的常见发动机舱内部件，被用于训练支撑ARabeitak移动应用的目标检测模型，该应用可直接在汽车发动机舱上叠加增强现实操作指引。

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## 数据集描述
### 应用场景
- 训练与评估**目标检测（object detection）**模型，具体应用包括：
- 汽车维修交互式增强现实指引
- 智能汽车手册/远程技术支持
- 面向基础汽车知识科普的教育类应用

### 数据模态
- **图像**：汽车发动机舱及相关零件的红-绿-蓝（RGB）彩色照片。

### 数据集结构
- 图像按照汽车零件/拍摄视角分为**7个文件夹**
- 这7个文件夹分别对应下文列出的标注目标类别。

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## 目标类别
本数据集共包含以下**7个目标类别**，下方数字为对应类别的当前图像样本量：

| 类别标签 | 样本量 |
|----------------------|-------|
| 空气滤清器（Air Filter） | 206 |
| 蓄电池（Battery） | 269 |
| 冷却液储液罐（Coolant Reservoir） | 296 |
| 发动机（Engine） | 212 |
| 发动机盖板（Engine Cover） | 187 |
| 储液罐盖（Reservoir Cap） | 11 |
| 挡风玻璃清洗液罐（Windshield Fluid） | 171 |

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## 数据样本说明
每个数据样本包含：
- **图像**
- 从用户视角拍摄的汽车发动机舱内部照片
- 拍摄环境为真实工况：存在灰尘、阴影、光照变化等情况

## 采集流程
### 数据来源
- 照片拍摄自2015款丰田卡罗拉的发动机舱
- 使用消费级智能手机（三星Galaxy S20+）在自然光照环境下拍摄，未使用专业影棚设备。