Real-time Object Detection

 Real-time Object Detection – IT and Computer Engineering Guide

1. Project Overview

Objective: Build a system capable of detecting and identifying objects in real-time using pre-trained models such as YOLO (You Only Look Once) or MobileNet.
Scope: Applications include security systems, autonomous navigation, and augmented reality.

2. Prerequisites

Knowledge: Familiarity with Python, deep learning, and real-time video processing.
Tools: Python, OpenCV, TensorFlow/Keras, PyTorch, and pre-trained YOLO or MobileNet models.
Hardware: A system with a capable GPU for real-time inference.

3. Project Workflow

- Model Selection: Choose a pre-trained model like YOLOv5 or MobileNet-SSD.

- Setup Environment: Install necessary dependencies and frameworks.

- Input Processing: Read video feed or webcam input and preprocess frames.

- Inference: Use the model to detect objects in each frame.

- Post-processing: Draw bounding boxes and labels on detected objects.

- Performance Optimization: Optimize for real-time processing on your hardware.

4. Technical Implementation

Step 1: Install Dependencies


# Install required libraries
!pip install opencv-python opencv-python-headless tensorflow torch torchvision

Step 2: Load the Pre-trained Model


# Example for YOLOv5
from ultralytics import YOLO

# Load the YOLO model
model = YOLO('yolov5s.pt')  # Replace with your model's path or identifier

Step 3: Process Video Feed


import cv2

# Open webcam
cap = cv2.VideoCapture(0)
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
   
    # Perform detection
    results = model(frame)
   
    # Annotate frame
    annotated_frame = results[0].plot()
   
    # Display frame
    cv2.imshow("Real-time Object Detection", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Step 4: Post-processing and Labeling


# Example of labeling
for result in results.xyxy[0]:  # Iterate over detections
    x1, y1, x2, y2, confidence, class_id = map(int, result[:6])
    label = f"{model.names[class_id]}: {confidence:.2f}"
    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

Step 5: Optimize for Real-Time Performance


# Techniques for optimization:
- Use a GPU for inference.
- Reduce model size or input image resolution.
- Batch process multiple frames.

5. Results and Insights

Evaluate the system's performance in terms of frame rate, accuracy, and robustness under different lighting and environmental conditions.

6. Challenges and Mitigation

High Latency: Reduce model size or input resolution.
False Positives/Negatives: Fine-tune the model or adjust confidence thresholds.

7. Future Enhancements

Incorporate edge AI devices like NVIDIA Jetson Nano for deployment.
Expand to multi-camera setups or multi-class tracking.

8. Conclusion

The Real-time Object Detection project highlights the integration of computer vision and deep learning, showcasing applications in diverse fields like security and autonomous systems.