The YOLOv8 Car Object Detection Dataset provides a large collection of images annotated with bounding boxes for cars across diverse real-world environments, supporting the development of robust AI models for autonomous driving, robotics, and surveillance.

nihany/car-object-detection dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contians labelled vehicle images gathered from Kaggle, Standford Car Dataset and Web scraping.

Dataset size: 3000 images Each class: 500 images Format: YOLO (txt) Train/Valid split: 70%(2100 images) : 30 (900 images)

Classes:

Car Threewheel Bus Truck Motorbike Van

The Traffic Vehicles Object Detection dataset is a valuable resource containing 1,201 images capturing the dynamic world of traffic, featuring 11,134 meticulously labeled objects. These objects are classified into seven distinct categories, including common vehicles like car, two_wheeler, as well as blur_number_plate, and other essential elements such as auto, number_plate, bus, and truck. The dataset's origins lie in the collection of training images from traffic scenes and CCTV footage, followed by precise object annotation and labeling, making it an ideal tool for object detection tasks in the realm of transportation and surveillance.

Vehicle Detection Image Dataset

Introduction

Welcome to the Vehicle Detection Image Dataset! This dataset is meticulously curated for object detection and tracking tasks, with a specific focus on vehicle detection. It serves as a valuable resource for researchers, developers, and enthusiasts seeking to advance the capabilities of computer vision systems.

Objective

The primary aim of this dataset is to facilitate precise object detection tasks, particularly in identifying and tracking vehicles within images. Whether you are engaged in academic research, developing commercial applications, or exploring the frontiers of computer vision, this dataset provides a solid foundation for your projects.

Preprocessing and Augmentation

Both versions of the dataset undergo essential preprocessing steps, including resizing and orientation adjustments. Additionally, the Apply_Grayscale version undergoes augmentation to introduce grayscale variations, thereby enriching the dataset and improving model robustness.

1. Apply_Grayscale

• This version comprises grayscale images and is further augmented to enhance the diversity of training data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2F4f23bd8094c892d1b6986c767b42baf4%2Fv2.png?generation=1712264632232641&alt=media" alt="">

2. No_Apply_Grayscale

• This version includes images without applying grayscale augmentation.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2Fbfb10eb2a4db31a62eb4615da824c387%2Fdetails_v1.png?generation=1712264660626280&alt=media" alt="">

Data Formats

To ensure compatibility with a wide range of object detection frameworks and tools, each version of the dataset is available in multiple formats:

1. COCO
2. YOLOv8
3. YOLOv9
4. TensorFlow

These formats facilitate seamless integration into various machine learning frameworks and libraries, empowering users to leverage their preferred development environments.

Real-Time Object Detection

In addition to image datasets, we also provide a video for real-time object detection evaluation. This video allows users to test the performance of their models in real-world scenarios, providing invaluable insights into the effectiveness of their detection algorithms.

Getting Started

To begin exploring the Vehicle Detection Image Dataset, simply download the version and format that best suits your project requirements. Whether you are an experienced practitioner or just embarking on your journey in computer vision, this dataset offers a valuable resource for advancing your understanding and capabilities in object detection and tracking tasks.

Citation

If you utilize this dataset in your work, we kindly request that you cite the following:

Parisa Karimi Darabi. (2024). Vehicle Detection Image Dataset: Suitable for Object Detection and tracking Tasks. Retrieved from https://www.kaggle.com/datasets/pkdarabi/vehicle-detection-image-dataset/

Feedback and Contributions

I welcome feedback and contributions from the Kaggle community to continually enhance the quality and usability of this dataset. Please feel free to reach out if you have suggestions, questions, or additional data and annotations to contribute. Together, we can drive innovation and progress in computer vision.

This dataset is designed for the detection of persons and cars in surveillance camera footage. It can be utilized for various useful applications, including:

• Security Systems: Enhancing security measures by accurately detecting and tracking persons and cars in real-time surveillance videos.
• Traffic Monitoring: Analyzing traffic patterns, estimating congestion levels, and optimizing traffic flow by detecting and counting cars on roads or at intersections.
• Safety Enhancement: Identifying potential hazards and ensuring public safety by detecting unauthorized access or suspicious activities involving persons and cars.
• Crowd Management: Monitoring crowded areas and public events to ensure safety, identify crowd density, and estimate crowd movement by detecting and tracking persons.
• Parking Systems: Optimizing parking lot management by detecting available parking spots and monitoring the entry and exit of vehicles.
• Smart Cities: Contributing to the development of smart city infrastructure by integrating the detection of persons and cars into intelligent systems for efficient urban planning and management.

This dataset is based on images collected from various sources, including:

https://universe.roboflow.com/radoslaw-kawczak/virat-ve02s

https://universe.roboflow.com/seminar-object-detection/cars-o1ljf

With this dataset, you can train and develop machine learning models capable of accurately detecting persons and cars, thus empowering surveillance and security systems with advanced object recognition capabilities.

• This dataset contains 499 images, each with bounding box annotations for cars.
• The annotations are provided in the YOLO text format, which includes class labels and bounding box coordinates.
• This dataset is useful for object detection tasks such as vehicle recognition and traffic analysis.

Car Object Detection Project

## Overview

Car Object Detection Project is a dataset for object detection tasks - it contains Cars 1RJ0 annotations for 3,835 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Car Object Detection Dataset by GTS.AI is a comprehensive dataset featuring car images and videos captured from various regions and lighting conditions. Designed for computer vision, autonomous driving, and smart traffic systems, it supports deep learning applications for vehicle detection and localization. Each dataset undergoes manual annotation, quality control, and ISO-certified validation.

This dataset provides a collection of labeled aerial images of vehicles taken from drones in the campus of Prince Sultan University, Saudi Arabia.

The PSU dataset was collected from two sources: an open dataset of aerial images available on Github repo Aerial-car-dataset, available online on: https://github.com/jekhor/aerial-cars-dataset

and our own images acquired after flying a 3DR SOLO drone equipped with a GoPro Hero 4 camera, in an outdoor environment at PSU parking lot. The drone recorded videos from which frames were extracted and manually labeled. Since we are only interested in a single class, images with no cars have been removed from the dataset. The training/testing split was made randomly.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12447376%2F7f567f46d0bd35c3a3d5939356d28bee%2FScreenshot%20from%202023-08-29%2013-23-49.png?generation=1693304666549998&alt=media" alt=""> Total number of images: 270

If you use this dataset in a research paper, please cite the following reference: Ammar, A., Koubaa, A., Ahmed, M., Saad, A. and Benjdira, B., 2021. Vehicle detection from aerial images using deep learning: A comparative study. Electronics, 10(7), p.820.

This Kaggle dataset includes 1000 JPEG images of diverse cars, paired with 1000 text files containing bounding box annotations in YOLO format. It's designed for training object detection models to recognize cars, and is suitable for various computer vision applications and educational use.

Background Car

## Overview

Background Car is a dataset for object detection tasks - it contains Cars Background annotations for 920 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Vehicle detection is a very important aspect of computer vision application to aerial and satellite imagery, facilitating activities such as instance counting, velocity estimation, traffic predictions, etc. The feasibility of accurate vehicle detection often depends on limited training datasets, requiring a lot of manual work in collection and annotation tasks. Furthermore, there are no known publicly available datasets. Our aim was to construct a pipeline for synthetic dataset generation from aerial imagery and 3D models in Blender software. The dataset generation pipeline consists of seven steps and results in a wished number of images with bounding boxes in YOLO and coco formats. This synthetic dataset has been produced following the steps described in this pipeline. It consists of 5000 2048x2048 images with cars inserted into the roads and highways at the images without cars from all over the world. We believe that this dataset and the respective pipeline might be of great importance for vehicle detection, facilitating the customizability of the models to specific needs and context.

A comprehensive dataset of car images annotated for object detection tasks. It includes train, validation, and test sets with bounding box coordinates for each vehicle.

This dataset contains 29,800 labeled entries for vehicle detection tasks. Each entry provides bounding box coordinates for a car within an image, along with its dimensions and class label. The dataset is well-suited for object detection, vehicle counting, and traffic surveillance model training.

Columns:

• filename – Image file name containing the car(s)
• width – Width of the image in pixels
• height – Height of the image in pixels
• class – Object class label (e.g., "car")
• xmin – Left x-coordinate of the bounding box
• ymin – Top y-coordinate of the bounding box
• xmax – Right x-coordinate of the bounding box
• ymax – Bottom y-coordinate of the bounding box

Potential Uses:

• Training YOLO, Faster R-CNN, or SSD object detection models
• Vehicle recognition in CCTV footage
• Autonomous driving system datasets
• Traffic monitoring and analysis

Format: CSV file with 8 columns containing bounding box annotations for each object in an image.

At Driver Technologies, we specialize in collecting high-quality, highly-anonymized driving data crowdsourced through our dash cam app. Our Car Object Detection Video Data is built from millions of miles of driving data captured by our users and is optimized for training object detection models and enhancing various applications in transportation and safety.

What Makes Our Data Unique? What sets our Car Object Detection Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes vehicles, pedestrians, traffic signs, road conditions, and more, resulting in rich, annotated datasets that can be applied across a range of industries.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios

Primary Use-Cases and Verticals The Car Object Detection Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:

Training Object Detection Models: Clients can utilize our annotated data to develop and refine their own object detection models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.

Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic management.

Insurance Analytics: Insurance companies can leverage insights from our data to assess risk in various driving environments, aiding in the development of tailored insurance products and improving claims processing.

Integration with Our Broader Data Offering The Car Object Detection Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and object detection models.

In summary, Driver Technologies' Car Object Detection Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Car Object Detection Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

1. Overview This dataset is a collection of 100,000+ images of cars in multiple scenes that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Annotated Imagery Data of car images This dataset contains 4,000+ images of cars. Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands.

Light Car

## Overview

Light Car is a dataset for object detection tasks - it contains Cars LQdT annotations for 488 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Udacity Self-Driving Car Dataset has been updated to address issues in the original annotations, which were missing labels for several key objects such as pedestrians, bikers, cars, and traffic lights. These omissions can negatively impact model performance and, in the context of self-driving cars, could lead to dangerous scenarios. To solve this, the dataset has been re-labeled with accurate annotations, ensuring improved performance for computer vision models used in autonomous vehicle systems.

This dataset includes various formats for ease of use, including VOC XML, COCO JSON, TensorFlow Object Detection TFRecords, and more.

Dataset Statistics

• Total Labels: 97,942 across 11 classes
• Total Images: 15,000
• Null Examples: 1,720 (images with no labels)
• Image Resolution: 1920x1200 (approx. 3.1 GB download size) A downsampled version is also available at 512x512 (approx. 580 MB), suitable for common machine learning models like YOLO v3, Mask R-CNN, SSD, and MobileNet.

A meticulously curated and annotated dataset of car images, segmented into seven categories representing different makes and models. Designed for computer vision, image classification, and object detection applications.