CDNet is all you need: Cascade DCN based underwater object detection RCNN
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 25 Nov 2021.
Builds on
11 of 13 references.
References · 11
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2017Deformable Convolutional Networkscited by 1,481
- 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
- 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
- 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 428
- 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 289
- 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
- 2020Global Context-Aware Progressive Aggregation Network for Salient Object Detectioncited by 109
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Citing papers per year: 2023: 1.
1 paper.