https://github.com/digitalspy12/dspy_imcc_hackathon_2025

This is AI Reconnaissance in Miltary, The Recon tools which is a system of 5 step data collection, Ai processing, Algrorithms and this pillars create a Decision support for commander. This system used yolov11 which we can train and change according to need and fields.
https://github.com/digitalspy12/dspy_imcc_hackathon_2025

ai military object-detection reconnaissance yolov11

Last synced: 4 months ago
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This is AI Reconnaissance in Miltary, The Recon tools which is a system of 5 step data collection, Ai processing, Algrorithms and this pillars create a Decision support for commander. This system used yolov11 which we can train and change according to need and fields.

• Host: GitHub
• URL: https://github.com/digitalspy12/dspy_imcc_hackathon_2025
• Owner: Digitalspy12
• Created: 2025-02-05T15:32:08.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-02-16T16:20:49.000Z (4 months ago)
• Last Synced: 2025-02-16T17:50:19.027Z (4 months ago)
• Topics: ai, military, object-detection, reconnaissance, yolov11
• Language: TypeScript
• Homepage:
• Size: 1.13 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Military Object Detection Web App

A web application that uses YOLOv11 to detect military objects in images and visualize them on a map.

## Features

- Drag-and-drop image upload

- Military object detection using custom-trained YOLOv11

- Interactive map visualization (when geolocation data is available)

- Detection results panel with confidence scores

- Annotated image display

## Interface 

![WhatsApp Image 2025-02-16 at 8 13 24 AM](https://github.com/user-attachments/assets/9564e255-afb5-4c1b-87d3-5bbc82e29159)

## Demo

![4f7cb1f6-8284-4207-8801-84a904dfb338](https://github.com/user-attachments/assets/9a9f74de-e7c5-4d65-9019-a130a1708e99)

![030482c5-75fe-4c8e-9173-4b26eb6d629a](https://github.com/user-attachments/assets/ef083c63-7f00-4982-a75e-ccf8a5a58255)

![88520de0-5945-4284-abd2-92a3f712faa6](https://github.com/user-attachments/assets/46e2fc01-fa91-43ce-8b1b-989caa435d48)

![ca2e579d-e588-4e0f-a815-2f9b904de5f6](https://github.com/user-attachments/assets/91948314-3208-4409-940e-16aa48e6e7c5)

## Setup

### Backend Setup

1. Navigate to the backend directory:

```bash

cd backend

```

2. Create a virtual environment (optional but recommended):

```bash

python -m venv venv

source venv/bin/activate  # On Windows: venv\Scripts\activate

```

3. Install dependencies:

```bash

pip install -r requirements.txt

```

### Frontend Setup

1. Install Node.js dependencies:

```bash

npm install

```

## Running the Application

1. Start the backend server:

```bash

cd backend

python app.py

```

The backend will run on http://localhost:5000

2. In a new terminal, start the frontend development server:

```bash

npm run dev

```

The frontend will run on http://localhost:5173

3. Open your browser and navigate to http://localhost:5173

## Usage

1. Drag and drop an image or click to select one from your computer

2. Wait for the detection process to complete

3. View the results in the side panel:

   - List of detected objects with confidence scores

   - Total number of detections

   - Annotated image with bounding boxes

4. If the image contains geolocation data, detected objects will be displayed on the map

## Tech Stack

- Backend: Python, Flask, YOLOv11, OpenCV

- Frontend: React, TypeScript, Tailwind CSS

- Map: Leaflet with React-Leaflet