What is a feature pyramid?
Feature pyramid (Feature Pyramid) is a commonly used technology for tasks such as object detection, image segmentation, and target tracking in computer vision. Its main idea is to capture object information of different sizes and resolutions by extracting features at different image scales.
Specifically, the feature pyramid usually consists of the following steps:
- Image pyramid: First, multi-scale downsampling or upsampling is performed on the input image to generate an image pyramid, in which each layer corresponds to a different scale.
Feature extraction network: A feature extraction network, such as a convolutional neural network (CNN), is applied at each scale to extract features at that scale. - Feature fusion: Fusion of features at different scales to obtain a set of feature pyramids with information at different scales.
- Model training and inference: In the training phase, the feature pyramid is input into target detection, image segmentation and other models for training; in the inference phase, the feature pyramid is used to extract target features and perform tasks such as target detection and image segmentation.
- The advantage of feature pyramid is that it can process images at different scales simultaneously, allowing objects of different sizes to be detected. At the same time, by fusing features of different scales, the model's anti-noise ability and adaptability to object shapes can be improved.
Is upsampling or downsampling equal?
Not necessarily. In an image pyramid, upsampling or downsampling can be done in an equiproportional or non-proportional manner.
Proportional upsampling or downsampling means that the size of the image changes in the same proportion at each scale, such as shrinking it by half or enlarging it by a factor of two. This method can keep the geometry of the image unchanged, but may result in the loss or redundancy of some information. Non-proportional upsampling or downsampling allows the image size to vary to varying degrees at each scale. For example, in an object detection task, to accommodate targets of different sizes, one can perform larger downsampling on low-resolution images and smaller downsampling on high-resolution images. This method can better capture target information at different scales, but may destroy the geometry of the image.
Therefore, choosing an equal-proportional or non-equal-proportional upsampling or downsampling strategy depends on the specific task requirements and application scenarios.