Space Station Object Detection

📋 Table of Contents

1. 🤖 Introduction
2. ⚙️ Tech Stack
3. 🔋 Features
4. 🤸 Quick Start
5. 🔗 Assets
6. 🚀 More

🤖 Introduction

This project is a simulation-based object detection system designed to detect critical objects inside a space station environment. Built during a hackathon, the model is capable of identifying essential equipment under challenging conditions using computer vision and AI.

If you're getting started and need assistance or face any bugs, join our active developers community with over Team members. It's a place where people help each other out.

⚙️ Tech Stack

• Next js
• Fast API
• Typescript
• Tailwind CSS
• Python
• YOLOv8

🔋 Features

Features of the Space Station Object Detection

👉 Real-time image detection using OpenCV and Python with live webcam integration

👉 Displays object name and detection accuracy with visual overlays

👉 Success notifications for correctly identified items with confidence thresholds

👉 Warning alerts for missing or unidentified critical objects

👉 Clean and responsive UI built for ease of interaction and real-time feedback

👉 Modular architecture with reusable Python components for detection pipelines

👉 Secure and scalable design built for integration with broader systems

and many more, built for scalability and a smooth user experience.

🤸 Quick Start

Follow these steps to set up the project locally on your machine.

Prerequisites

Make sure you have the following installed on your machine:

• Git
• Node.js
• npm (Node Package Manager)
• python

Cloning the Repository

git clone https://github.com/Flaxmbot/TestModel.git
cd TestModel

Installation

Install the project dependencies using npm:

npm install (for frontend)

pip install requirements.txt (for backend model)

Running the Project

npm run dev  (for frontend)

uvicorn main:app --host 127.0.0.1 --port 8000 --reload  (for backend model)
Or Just run -> python run_app.py (in terminal)

Open http://localhost:3000 in your browser to view the project.

🔗 Assets

• Assets used in the project can be found here

🚀 More

-- Predicted Output that you get after Training the YOLOv8 model [here] (https://drive.google.com/drive/folders/1-XXclms-iA7VKhw5H9Zsg-tBcDhWSSBb?usp=drive_link)

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