Some common facade systems used in computer vision include:
1. Deep neural networks: These are very popular in computer vision and have been used for tasks like image classification, object detection, and image segmentation.
2. Convolutional neural networks (CNN): CNNs are commonly used for image recognition tasks, especially in cases where spatial information is important. They are designed to process data with a grid-like structure, such as images.
3. Region-based Convolutional Neural Networks (R-CNN): R-CNN is a popular architecture for object detection. It combines deep CNNs with a region proposal network to identify object regions and classify them.
4. Faster R-CNN: This is an improved version of R-CNN that introduced a shared convolutional network to speed up the region proposal process. It achieves faster processing time while maintaining accuracy.
5. Single Shot MultiBox Detector (SSD): SSD is another efficient object detection method that uses a set of default bounding box shapes and sizes to detect objects in different scales and aspect ratios.
6. YOLO (You Only Look Once): YOLO is an object detection algorithm that applies a single neural network to the full image and predicts bounding boxes and class probabilities in one go, providing real-time object detection.
7. U-Net: U-Net is widely used for image segmentation tasks. It consists of a contracting path for capturing context and a symmetric expanding path for precise localization.
8. Mask R-CNN: Mask R-CNN is an extension of the Faster R-CNN architecture that adds a pixel-level segmentation branch. It enables instance segmentation by outputting binary masks for each object detected.
These are just some of the common facade systems used in computer vision, and there are many other architectures and techniques that are being developed and explored in the field.
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