Object detection is a computer vision task where AI identifies and locates objects in images or videos.
It goes beyond image classification by providing both the object type and its position within the frame, usually with bounding boxes.
How Object Detection Works
- Image Input: Feed an image or video frame into the AI system.
- Feature Extraction: Use deep learning models like CNNs to detect important features.
- Bounding Box Prediction: Draw boxes around detected objects.
- Classification: Identify the object within each box.
- Postprocessing: Refine detection results using techniques like Non-Maximum Suppression (NMS).
Advantages of Object Detection
- Automates object identification and tracking
- Works in real-time for video analysis
- Improves safety, surveillance, and automation
- Enables AI-powered analytics and decision-making
Disadvantages
- Requires large annotated datasets for training
- Can struggle with occluded or overlapping objects
- Computationally intensive for real-time applications
Real-World Examples
- Self-driving cars detecting pedestrians, vehicles, and traffic signs
- Security cameras identifying intruders
- Retail analytics tracking customers and products
- Medical imaging detecting anomalies
- Industrial automation for quality control
Conclusion
Object detection empowers AI to recognize and locate objects accurately, making computer vision applications more intelligent and interactive.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/