# Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network

arXiv: https://arxiv.org/abs/1806.04828 · HTML: https://mlsanity.com/paper/1806.04828/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 13 of 13)

- [Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss](https://mlsanity.com/paper/2101.11952/) (2021)
- [Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence](https://mlsanity.com/paper/2106.01883/) (2021)
- [The KFIoU Loss for Rotated Object Detection](https://mlsanity.com/paper/2201.12558/) (2022)
- [ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection](https://mlsanity.com/paper/2303.04989/) (2023)
- [Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss](https://mlsanity.com/paper/2103.11636/) (2021)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment](https://mlsanity.com/paper/2210.08487/) (2022)
- [AlphaRotate: A Rotation Detection Benchmark using TensorFlow](https://mlsanity.com/paper/2111.06677/) (2021)
- [Ship in Sight: Diffusion Models for Ship-Image Super Resolution](https://mlsanity.com/paper/2403.18370/) (2024)
- [TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection](https://mlsanity.com/paper/2104.11435/) (2021)
- [Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing Images](https://mlsanity.com/paper/2307.06566/) (2023)
- [Edge Wasserstein Distance Loss for Oriented Object Detection](https://mlsanity.com/paper/2312.07048/) (2023)
- [Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection](https://mlsanity.com/paper/2604.08800/) (2026)

## Abstract (arXiv)

Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multitask rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design Adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS for rotation detection shows that our detection method has a competitive performance.