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.