Object detection is one of the main problems in computer vision that may fail when there are multi-scale objects in images. Using feature pyramids helps to solve this problem.

Some previous studies tried to use different kinds of feature pyramids to improve object detection. One method fed various sizes of the input image to the deep network to see objects with different scales. This way also helped improve object detection but increases computational costs and processing time so much that it is not efficient.

Feature pyramid network(FPN) was introduced by Tsung-Yi Lin et al., which enhanced object detection accuracy for deep convolutional object detectors. FPN solves this problem by generating a bottom-up and a top-down feature hierarchy with lateral connections from the network’s generated features at different scales. This helps the network generate more semantic features, so using FPN helps increase detection accuracy when there are objects with various scales in the image while not changing detection speed.

_Here, I aim to introduce a new architecture based on FPN to improve classification accuracy. This architecture is proposed in my _paper.

As described, FPN helps extract multi-scale features from the input image, which better presents objects with different scales. We have designed an architecture that utilizes FPN to understand better the important parts of the image that could exist in different sizes.

In the next figure, you can see our proposed architecture. This architecture was developed for classifying the patient CT scan images into normal and COVID-19. Researchers can modify this architecture for using on different datasets and classes.

#image-classification #neural-networks #classification #deep-learning #machine-learning