Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

Abstract

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

14 pages, 11 figures

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• SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
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• A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box
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• IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection
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• Oriented object detection in optical remote sensing images using deep learning: a survey
• Objects detection for remote sensing images based on polar coordinates
• EARL: An Elliptical Distribution aided Adaptive Rotation Label Assignment for Oriented Object Detection in Remote Sensing Images
• Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
• Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
• PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
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• Salience Biased Loss for Object Detection in Aerial Images
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• A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets
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• Oriented Boxes for Accurate Instance Segmentation
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• Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
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• Toward Autonomous Rotation-Aware Unmanned Aerial Grasping
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• Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multi-Scale Convolutional Network
• A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance