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YOLO Object Detection Overview
geeksforgeeks.org/computer-vision/how-does-yolo-work-for-object-detection/Yapay zekadan makale özeti
- Introduction and Evolution
- YOLO was introduced by Joseph Redmon et al. in 2015
- YOLO has evolved through versions YOLOv2, YOLOv3, YOLOv4, and YOLOv5
- Each iteration improved accuracy, speed, and detection capabilities
- Architecture and Process
- Single CNN predicts multiple bounding boxes and class probabilities
- Image is divided into SxS grid cells for detection
- Each cell predicts B bounding boxes with confidence scores
- Final predictions combine bounding boxes and class probabilities
- Non-max suppression removes redundant bounding boxes
- Advantages
- Processes entire image in single pass for real-time applications
- Enables end-to-end training directly on detection task
- Achieves high accuracy through simultaneous predictions
- Can detect multiple objects in images
- Applications
- Used in autonomous vehicles for navigation
- Applied in security systems for surveillance
- Helps in retail analytics and inventory management
- Assists in medical imaging for abnormalities detection