AR Doodle detection Title for Mini Project

AR Doodle Detection ppt

1. AR Doodle
Detection model
Team.exe

2. Introduction
Imagine a world where your hand-drawn doodles seamlessly come to life, transcending the boundaries of paper and ink to manifest
in the dynamic realm of Augmented Reality. Our project embarks on a mission to transform this imaginative vision into a tangible
reality, harnessing the power of advanced technologies to bridge the gap between physical artistry and digital interactivity.
The essence of our project lies in its unique ability to detect and interpret hand-drawn doodles, capturing their essence with
precision and elegance. Through a sophisticated fusion of machine learning algorithms, pattern recognition, and spatial awareness,
we have constructed a robust model that can identify doodles in real-time. But our endeavor doesn't halt there –we take this
recognition a step further by seamlessly integrating these doodles into the immersive canvas of Augmented Reality.
In this presentation, we will embark on a captivating journey through the various facets of our AR Doodle Detection Model. Wewill
delve into the intricacies of our methodology, exploring how the fusion of Computer Vision and AR technologies unlocks new
dimensions of creativity and interactive expression. Join us as we unravel the technical intricacies, the innovative leaps, and the
boundless potential that our project brings to the world of art, education, and entertainment. Welcome to a realm where the lines
between reality and imagination blur, and doodles become a gateway to immersive experiences –welcome to the AR Doodle
Detection Model.

3. Augmented Reality
and computer vision
What is augmented reality and computer vision?

4. What is augmented reality and computer
vision?
Augmented Reality (AR):
Augmented Reality, often abbreviated as AR, is a transformative technology that overlays digital content onto the real world,
enhancing our perception and interaction with the environment. Unlike virtual reality, which creates entirely immersive digital
environments, AR enhances our physical surroundings by seamlessly integrating virtual elements into our visual field. Throughthe
lens of AR, our perception of reality is enriched with digital information, offering a unique blend of the physical and virtual realms. AR
has found its way into various industries, from entertainment and gaming to education and healthcare, revolutionizing how we learn,
create, and engage with our surroundings.
Computer Vision:
Computer Vision is a dynamic field of artificial intelligence that equips computers with the ability to interpret visual information from
the world around them. By mimicking the human visual system, computer vision algorithms process and analyze images and videos
to extract meaningful insights and information. This field enables machines to perceive, understand, and make decisions basedon
visual input, ranging from object detection and facial recognition to scene understanding and image generation. With applications
spanning autonomous vehicles, medical diagnostics, and even artistic expression, computer vision empowers machines to interact
with and comprehend the visual world in a manner reminiscent of human perception.

5. Use Cases and Applications of AR Doodle Detection Model:
Interactive Learning and Education:
Imagine a classroom where students can illustrate complex concepts and watch them come alive in AR. The AR Doodle Detection
Model can be integrated into educational apps, enabling students to visualize scientific phenomena, historical events, and abstract
concepts through their doodles. This interactive approach enhances comprehension and engagement, making learning an
immersive and memorable experience.
Artistic Expression and Creative Design:
Artists and designers can leverage the AR Doodle Detection Model to bring their sketches to life in an augmented space. Users
can doodle intricate designs, characters, or scenes and instantly witness their creations materialize in three-dimensional AR
environments. This fusion of traditional artistry and digital interactivity opens new avenues for creative expression and collaboration.
Healthcare and Medical Training:
Medical practitioners and students can benefit from interactive anatomy lessons enabled by the AR Doodle Detection Model.
Doodled diagrams of organs, systems, and medical procedures can be projected onto physical models, enhancing the
understanding and visualization of complex medical topics.

6. Implementation Steps of the AR Doodle
Detection Model
Dataset Collection and Annotation:
Curate a diverse dataset of hand-drawn doodles encompassing various objects, shapes, and patterns. Annotate each image by
specifying the coordinates of bounding boxes around the doodles. These annotations serve as ground truth for YOLO's object
detection training.
Data Preprocessing and Augmentation:
Preprocess the dataset by resizing images to a consistent dimension and normalizing pixel values. Apply data augmentation
techniques such as rotation, scaling, and flipping to augment the dataset's diversity and enhance model robustness.
YOLO Configuration:
Adapt the YOLOv3 or YOLOv4 architecture for your doodle detection task. Modify the model's configuration file to align with the
number of classes (doodles) in your dataset. Set hyperparameters such as anchor sizes, strides, and filters based on your use
case.
Training the YOLO Model:
Train the YOLO model on the annotated dataset using a machine learning framework like Darknet or YOLOv5. Monitor loss metrics
during training to gauge model convergence and performance. Adjust learning rates and epochs as necessary.

7. Implementation Steps of the AR Doodle
Detection Model
Augmented Reality Integration:
Develop an AR application using Unity and AR Foundation. Configure the application to access the device's camera feed and
overlay AR content. Establish a pipeline for communicating between the YOLO model and Unity.
Real-time Doodle Detection:
Implement a mechanism to invoke the YOLO model's inference on the camera feed frames. Process the model's output to extract
bounding box predictions for detected doodles. Translate the bounding box coordinates to AR world coordinates.

8. Software Requirements
Python:
Python is the primary programming language for implementing the AR Doodle Detection Model, training machine learning models,
and interacting with various libraries and frameworks.
OpenCV:
OpenCV (Open Source Computer Vision Library) is essential for image processing, computer vision tasks, and real-time video
capture.
TensorFlow or PyTorch:
Choose a deep learning framework like TensorFlow or PyTorchfor training the object detection model, such as YOLO, and for any
machine learning-related tasks.
Darknet or YOLOv5:
If using YOLO for object detection, you'll need either the Darknet framework (for YOLOv3) or YOLOv5 repository (for YOLOv5) for
model training.

9. Software Requirements
Unity and AR Foundation:
Unity is used to develop the Augmented Reality application. AR Foundation is a Unity package that enables cross-platform AR
development, including integration with ARCoreand ARKit.
IDE (Integrated Development Environment):
Choose an IDE like Visual Studio Code, PyCharm, or JupyterNotebook for coding, debugging, and running your Python scripts.
Annotation Tools:
Use annotation tools like LabelImgor RectLabelto annotate your doodle dataset with bounding box coordinates.
Version Control (Optional):
Use version control systems like Git and platforms like GitHub to manage your codebase and collaborate with team members

10. Hardware Requirements
Computer:
A computer with sufficient processing power and memory is required for training machine learning models and running resource-
intensive tasks.
Webcam or Camera:
A webcam or camera is needed for capturing real-time video feeds for testing and integrating the model with Augmented Reality.
Smartphone or AR Device (Optional):
If testing your AR application on mobile devices or AR glasses, you'll need the respective hardware for deployment and user
interaction.

11. Conclusion
In the realm of Augmented Reality and Computer Vision, our journey to create the AR Doodle Detection Model has been one of
innovation, artistry, and technological convergence. Our project sets out to bridge the gap between traditional doodles and the
immersive world of Augmented Reality, and in doing so, it opens new horizons for creativity, education, and entertainment.
Through meticulous dataset curation, model training, and Augmented Reality integration, we have succeeded in crafting a solution
that marries the art of doodling with the boundless possibilities of technology. The AR Doodle Detection Model represents a union
between human expression and digital precision, enabling users to see their hand-drawn creations come alive in the dynamic
canvas of AR.
Our implementation showcases the seamless interaction between the YOLO object detection model and Unity's AR Foundation,
resulting in an experience that is both visually captivating and intellectually stimulating. From interactive learning tools that bring
educational concepts to life, to artistic platforms that empower creators to fashion immersive experiences, the potential applications
of the AR Doodle Detection Model are limitless.

12. Thankyou!