https://github.com/mallickboy/ai-powered-surveillance-vehicle
AI-powered surveillance vehicle with ESP32-CAM for real-time streaming and YOLO-based object detection. Features robotics-based GPIO control, web APIs for remote control via PyQt5, and real-time detection using multi-threading. Backend and ML-driven design. https://youtu.be/T7p0-L5nbjI (ML test) https://youtube.com/shorts/IsJDYwo2vSI (Vehicle Live)
https://github.com/mallickboy/ai-powered-surveillance-vehicle
arduino-ide desktop-application esp-32-cam gpio-pins iot machine-learning multithreading pyqt5 python remote-control robo yolov10
Last synced: 7 months ago
JSON representation
AI-powered surveillance vehicle with ESP32-CAM for real-time streaming and YOLO-based object detection. Features robotics-based GPIO control, web APIs for remote control via PyQt5, and real-time detection using multi-threading. Backend and ML-driven design. https://youtu.be/T7p0-L5nbjI (ML test) https://youtube.com/shorts/IsJDYwo2vSI (Vehicle Live)
- Host: GitHub
- URL: https://github.com/mallickboy/ai-powered-surveillance-vehicle
- Owner: mallickboy
- Created: 2024-11-27T06:36:08.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T12:46:50.000Z (9 months ago)
- Last Synced: 2025-04-04T08:03:03.281Z (7 months ago)
- Topics: arduino-ide, desktop-application, esp-32-cam, gpio-pins, iot, machine-learning, multithreading, pyqt5, python, remote-control, robo, yolov10
- Language: Python
- Homepage: https://youtube.com/shorts/IsJDYwo2vSI?feature=share
- Size: 31.1 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
AI-Powered Surveillance Vehicle
## Overview
This project is an AI-powered surveillance vehicle equipped with a 360° movable camera, enabling real-time control and object detection. The system leverages ESP32-CAM for movement and streaming, YOLO for object detection, and a PyQt-based desktop application for control and monitoring. The vehicle is controlled through custom API routes to manage motors, servos, lights, and stream the camera feed.
## Features
- 360° mobility for the surveillance vehicle
- Real-time video streaming at 720p
- YOLO-based object detection for identifying objects in the stream
- Control via web API (POSTMAN tested)
- PyQt desktop application with multi-threading for smooth control and monitoring
## API Endpoints
### Motor Control:
- `http://192.168.137.234/right_motors?control=0&value=80`
Controls the right motor.
- `control=0`: Stop/reset
- `control=1`: Move forward
- `control=-1`: Move backward
- `value`: Speed level (0-100)
- `http://192.168.137.234/left_motors?control=1&value=100`
Controls the left motor.
- `control=0`: Stop/reset
- `control=1`: Move forward
- `control=-1`: Move backward
- `value`: Speed level (0-100)
### Servo Motors:
- `http://192.168.137.234/servo_motors?control=1&value=90`
Controls the servo motors.
- `control=1`: Move servo
- `value`: Degree adjustment (±90 degrees from the last position)
### LED Control:
- `http://192.168.137.234/led_blink?control=2&value=800`
Controls the LED blinking.
- `control=2`: Blink
- `value`: Frequency of blinking (in ms)
### Flash Light:
- `http://192.168.137.234/flash_light?control=1&value=85`
Controls the flash light.
- `control=1`: Turn on
- `value`: Brightness level (0-100)
### Video Stream:
- `http://192.168.137.234:81/stream`
Streams the live video feed from the camera.
## Hardware
- **ESP32-CAM**: Microcontroller with a built-in camera used for live video streaming.
- **Motors and Servos**: For vehicle movement and camera angle adjustments.
- **LEDs**: Used for illuminating the surroundings.
## Software
- **Firmware**: Developed using C/C++ on the ESP32 using the Arduino IDE.
- **Backend**: Custom web server created with API endpoints for vehicle control and interaction.
- **Desktop Application**: Built with PyQt, handling real-time control, video stream, and YOLO object detection.
- **Object Detection**: Implemented using YOLO for detecting objects in real-time within the video feed.
## Tech Stack
- **Firmware**: C/C++
- **Software**: Web API, API Development, Postman, Multi-threading
- **AI**: YOLO, Object Detection
- **UI**: PyQt, UI/UX
## Installation
### Firmware Installation
1. Download and install the Arduino IDE.
2. Install the ESP32 board manager in Arduino IDE.
3. Upload the `ESP32-CAM` code to the device via USB.
4. Connect the vehicle’s hardware components (motors, LEDs, servos) to the appropriate GPIO pins.
### Desktop Application Installation
1. Clone this repository:
```bash
git clone https://github.com/your-username/AI-Powered-Surveillance-Vehicle.git
cd AI-Powered-Surveillance-Vehicle
```
2. Install dependencies:
```bash
For Pyhon Environment : Numpy, OpenCV, PyQt5, Ultralytics, YOLOv10 model etc.
For Arduino IDE: AsyncTCP, ESP Async WebServer, ESP32Servo etc.
```
3. Start the ESP32 vehicle and the pyqt desktop application.
4. Get the IP address of ESP32 from your router/ phone/ Laptop.
5. Enter the IP address in the PyQt Desktop Application.
## Usage
- Control the vehicle using the desktop application.
- Use the provided API routes to interact with the vehicle remotely, such as controlling motors, LEDs, or servo positions.
## Future Enhancements
- Improved object detection models for better accuracy and speed.
- Integration with cloud services for remote control.
- Voice-based control interface.
## Contributors
- **Tamal Mallick** : *Firmware coding, Software coding, Machine Learning Integration, Wiring, Project Planning, Report & PPT making, Background Research, Others*
- **Avishek Mondal** : *3D Printing, Circuit Design, Hardware Assembly, Wiring, Project Planning, PPT making, Background Research, Others*
- **Souvik Baidya** : *Project Planning, Co-ordination, Report & PPT making, Background Research, Others*