0

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

Cascade-DCN, a combination of Cascade-RCNN and Deformable Convolution Network, is evaluated for object detection on various underwater image datasets with engineering tricks and augmentations.

Year
2021
Venue
arXiv 2021
Authors
1
Hosting
Abstract onlyARXIV-DEFAULT

Cite

Notes

Only stored in your browser.

Attribution

Abstract & full text
arxiv.org/abs/2111.12982ARXIV-DEFAULT
TL;DR
Semantic Scholar
Attribution policy →

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.

Authors

1