Object detection is a fundamental task in computer vision, where the goal is to identify and locate objects within an image or a video. This technology has numerous applications, from autonomous vehicles to security systems. In this introduction, we will explore the basics of object detection and its significance.
Key Concepts
- Bounding Boxes: A bounding box is a rectangle that surrounds an object in an image. It is used to define the location of an object.
- Confidence Score: This score indicates the probability that the detected object is indeed the object it is labeled as.
- Non-maximum Suppression (NMS): A technique used to eliminate duplicate detections, ensuring that only the most confident detections are kept.
Challenges
Object detection faces several challenges, including:
- ** Occlusions**: Objects can be partially or fully obscured by other objects.
- Scale Variability: Objects can vary significantly in size within an image.
- Complex Backgrounds: Objects can be difficult to detect when they are surrounded by complex backgrounds.
Applications
Object detection has a wide range of applications, including:
- Autonomous Vehicles: Detecting and avoiding obstacles on the road.
- Security Systems: Monitoring for suspicious activity.
- Retail: Counting customers and inventory.
- Healthcare: Detecting diseases from medical images.
Further Reading
For more information on object detection, you can explore the following resources:
Object Detection Example