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

TL;DR

This paper introduces a multitask rotation region CNN for ship detection that effectively handles dense, arbitrarily oriented ships and predicts their direction, improving detection accuracy in complex remote sensing scenarios.

Contribution

The paper presents a novel multitask rotation region CNN with adaptive ROI Align and rotational NMS, specifically designed for dense and arbitrarily oriented ship detection and direction prediction.

Findings

Achieves competitive performance on SRSS dataset.

Effectively detects dense, arbitrarily oriented ships.

Predicts ship berthing and sailing directions.

Abstract

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.