# CDNet is all you need: Cascade DCN based underwater object detection RCNN

Source: [CDNet is all you need: Cascade DCN based underwater object detection RCNN](<https://www.sota2.com/research/paper/68fa6b501e209131cea2d8d9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:11:34.522Z

Authors: Di Chang

Published: 2021-11-25T09:35:27.000Z

## Abstract

Object detection is a very important basic research direction in the field of computer vision and a basic method for other advanced tasks in the field of computer vision. It has been widely used in practical applications such as object tracking, video behavior recognition and underwater robotics vision. The Cascade-RCNN and Deformable Convolution Network are both classical and excellent object detection algorithms. In this report, we evaluate our Cascade-DCN based method on underwater optical image and acoustics image datasets with different engineering tricks and augumentation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.12982>)

[PDF](<https://arxiv.org/pdf/2111.12982.pdf>)