Cascade R-CNN

Cascade R-CNN is an object detection model that is an extension of the popular Region-based Convolutional Neural Network (R-CNN) model. The Cascade R-CNN model improves upon the R-CNN model by using a cascade of multiple stages of detectors that refine the detection results at each stage.

In the first stage of the Cascade R-CNN model, a relatively fast and less accurate detector is used to reject a large number of non-object regions. In the second stage, a more accurate detector is used to further reject non-object regions while detecting most of the true object regions. In the third stage, the most accurate detector is used to detect the remaining difficult object regions.

The Cascade R-CNN model has been shown to achieve state-of-the-art performance on various object detection benchmarks, such as COCO and PASCAL VOC. It is widely used in applications such as autonomous driving, surveillance, and robotics.