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A self-driving car (autonomous vehicle, AV, autonomous car, driver-less car, robotic car, or robo-car), incorporates vehicular automation which is the capability of sensing its environment and moving with little or no human input.
Many aspects are involved in deploying this core technology. The car is equipped with sensors, cameras, a compute engine, and decision algorithms that surround the deep learning technology. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.
Use datasets, pre-trained models, repos, courses, and tutorials in this page as an aid to your self-driving projects.
Courses to learn more about autonomous vehicles
Blog posts and videos
This is an Instance Segmentation project for visualizing detected cracks on concrete. This dataset is usable for those doing transportation and public safety studies, creating self-driving car models, or testing out computer vision models for fun.
Check out this example guide from Augmented Startups to see the model in action and learn how the dataset came together: https://medium.com/augmented-startups/yolov7-segmentation-on-crack-using-roboflow-dataset-f13ae81b9958
This project was created by downloading the GTSDB German Traffic Sign Detection Benchmark
dataset from Kaggle and importing the annotated training set files (images and annotation files)
- Original home of the dataset: https://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset - Institut Für Neuroinformatik
v1 contains the original imported images, without augmentations. This is the version to download and import to your own project if you'd like to add your own augmentations.
v2 contains an augmented version of the dataset, with annotations. This version of the project was trained with Roboflow's "FAST" model.
v3 contains an augmented version of the dataset, with annotations. This version of the project was trained with Roboflow's "ACCURATE" model.
This is a U.S. license plate dataset + model using object detection. The images for this dataset were collected from Google images and around Central Florida parks. If you see your license plate in this dataset and you wish to remove it, please contact firstname.lastname@example.org
This model helps detect people riding bycicles, and from which direction the cyclist can be seen (front, back, side).
Both self driving and sports broadcasting are great use cases for this model, as it gives great information about how the camera is positioned relative to the rider(s).
Self-Driving Thermal Object-Detection
This model detects potentially moving objects (cars, bicycles, people, and dogs), to aid in self-driving and autonomous vehicles.
The dataset is comprised of over twelve thousand thermal images, largely annotating cars.
This is a collection of 665 images of roads with the potholes labeled. The dataset was created and shared by Atikur Rahman Chitholian as part of his undergraduate thesis and was originally shared on Kaggle.
Note: The original dataset did not contain a validation set; we have re-shuffled the images into a 70/20/10 train-valid-test split.
This dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.
The dataset is provided in a wide variety of formats for various common machine learning models.
The Numberplate Dataset is a collection of Licence Plates that can easily be used for Automatic Number Plate Detection.
Training and Deployment
The Number Plate model has been trained in Roboflow, and available for inference on the Dataset tab.
One could also build a Automatic Number Plate Recognition [ANPR] App using YOLOR and EasyOCR. This is achieved using the Roboflow Platform which you can deploy the model for robust and real-time ANPR.
About Augmented Startups
We are at the forefront of Artificial Intelligence in computer vision. With over 92k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality
Traffic and Road Sign Detection
This dataset is comprised of many different traffic and road signs.
Self-Driving and Autonomous Driving
The PKLot dataset contains 12,416 images of parking lots extracted from surveilance camera frames. There are images on sunny, cloudy, and rainy days and the parking spaces are labeled as occupied or empty. We have converted the original annotations to a variety of standard object detection formats by enclosing a bounding box around the original dataset's rotated rectangle annotations.
Using this Dataset
The PKLot database is licensed under a Creative Commons Attribution 4.0 License and may be used provided you acknowledge the source by citing the PKLot paper in publications about your research:
Almeida, P., Oliveira, L. S., Silva Jr, E., Britto Jr, A., Koerich, A., PKLot – A robust dataset for parking lot classification, Expert Systems with Applications, 42(11):4937-4949, 2015.
The Perse Real Cones image dataset is an annotated collection of over 1,500 images of orange traffic cones in a variety of settings.
Use this traffic cone detection dataset in your own work standalone or as an augmentation to an existing dataset. Popular projects related to traffic cones include factory safety, obstacle detection for autonomous vehicles, or robotics competitions.
Still looking for inspiration? Check out this blog post on some ways to apply computer vision to your robotics project: https://blog.roboflow.com/raspberry-pi-luxonis-oak-computer-vision/
Dataset collected from CARLA.
- Good for traffic light detection by color
- Good for traffic sign detection by speed
- Cars,trucks,... have been simplified to "vechicles"
- Bikes, motobikes and persons
More about me
You can find out more about me on my linkedin
This dataset contains 627 images of various vehicle classes for object detection. These images are derived from the Open Images open source computer vision datasets.
This dataset only scratches the surface of the Open Images dataset for vehicles!
- Train object detector to differentiate between a car, bus, motorcycle, ambulance, and truck.
- Checkpoint object detector for autonomous vehicle detector
- Test object detector on high density of ambulances in vehicles
- Train ambulance detector
- Explore the quality and range of Open Image dataset
Tools Used to Derive Dataset
These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.
We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.
The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.
We re-labeled the dataset to correct errors and omissions. We have provided convenient downloads in many formats including VOC XML, COCO JSON, Tensorflow Object Detection TFRecords, and more.
Some examples of labels missing from the original dataset:
The dataset contains 97,942 labels across 11 classes and 15,000 images. There are 1,720 null examples (images with no labels).
All images are 1920x1200 (download size ~3.1 GB). We have also provided a version downsampled to 512x512 (download size ~580 MB) that is suitable for most common machine learning models (including YOLO v3, Mask R-CNN, SSD, and mobilenet).
Annotations have been hand-checked for accuracy by Roboflow.
Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.
Using this Dataset
Our updates to the dataset are released under the MIT License (the same license as the original annotations and images).
Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).
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