LEGO BRICK COLOUR AND SIZE DETECTION

The goal of this project is to develop an automated system that can accurately detect and classify the color and size of LEGO bricks from images.

WORKFLOW

TECHNOLOGY USED:

• PYTHON PROGRAMMING
• YOLO ARCHITECHTURE

WHY WE PREFER PYTHON?

Python is used because of its simple syntax, extensive libraries, strong community support, and versatility across various applications like web development, data analysis, and machine learning. It’s also platform-independent and ideal for rapid prototyping.

WHY WE PREFER YOLO?

YOLO outperforms CNN and k-NN in object detection tasks by offering real-time speed, integrated object detection and localization, and the ability to handle multiple objects simultaneously. While CNNs are powerful for classification and feature extraction, and k-NN is simple and intuitive for classification, YOLO provides a more comprehensive and efficient solution for tasks that require both classification and localization, like predicting LEGO pieces in an image.

TO RUN:

git clone https://github.com/MidhunVS21/Lego-Detection.git
cd Lego-Detection
streamlit run app.py

LINK OF DEPLOYED APP:

• lego-detection.streamlit.app
• lego-app.streamlit.app

DEMONSTRATION VIDEO:

Web.Lego.Predict.OP.mp4

TEAM MEMBERS

• Midhun @MidhunVS
• Thavamani @THAVAMAN
• Junaitha Fathima @JunaithaFathima
• Deepthi Prasanna @DeepthiPrasanna