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YOLO: You Only Look Once
- With the advancement of technology, object detection plays an important role in the field of computer vision, which is a branch of machine learning focused on processing visual data.
- YOLO is a real-time object detection algorithm that has become widely used in various applications and is known for its speed and accuracy.
- The algorithm simplifies detection by creating a single neural network that can process an entire image in one pass, predicting object locations and classes simultaneously.
- By dividing the image into a grid, YOLO can localize objects efficiently, trading some accuracy for significant speed improvements.
- Non-Maximum Suppression (NMS) is applied to remove redundant or overlapping bounding boxes, keeping only the most confident detections for each object.
- YOLOv1 divided an input image into a 7x7 grid, whereas YOLOv2 or YOLO9000 introduced anchor boxes and hierarchical classification and localization.
- YOLOv4 introduced numerous state-of-the-art techniques, including CSPDarknet53 as the backbone, Spatial Pyramid Pooling (SPP) for improved feature extraction, and Path Aggregation Network (PANet) for better feature fusion.
- Developed by Ultralytics, YOLOv5 prioritized usability, offering a streamlined training pipeline and integration with modern frameworks.
- YOLOv8 introduces an anchor-free design, simplifying training and enhancing detection for varying object sizes. The architecture incorporates the C2f module, an evolution of CSPNet.
- The YOLOv9 introduces two key innovations to address information loss in deep learning: Programmable Gradient Information (PGI) and a novel architecture called Generalized Efficient Layer Aggregation Network (GELAN).
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