Automatic Targeting System
A solution to capture live footage from camera and identify object using YOLO algorithm and target the object with NodeMCU controlled actuators.
Technologies Used
Overview
An intelligent automatic targeting system that combines computer vision and IoT hardware to detect and track objects in real-time using the YOLO (You Only Look Once) algorithm.
Key Features
- Real-time Object Detection: Uses YOLO algorithm for fast and accurate object detection
- Live Camera Feed: Continuous video capture and processing
- Automated Targeting: Hardware actuators automatically target detected objects
- Hardware Integration: NodeMCU-controlled servo motors for precise positioning
Technical Implementation
The system captures live video feed using a camera and processes each frame using the YOLO object detection algorithm implemented in Python with OpenCV. When an object is detected, the system calculates the object’s position and sends control signals to NodeMCU-controlled servo motors, which adjust the targeting mechanism to point at the detected object.
Technologies Used
- Python: Core application logic and image processing
- YOLO: State-of-the-art object detection algorithm
- OpenCV: Computer vision and video processing
- NodeMCU: Microcontroller for hardware control
- Servo Motors: Precision actuators for targeting
Applications
This project demonstrates the integration of machine learning, computer vision, and IoT hardware, with potential applications in security systems, automated tracking, and robotics.