Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
TL;DR: This work proposes a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port and proposes multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information.
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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.
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Citations
Learning RoI Transformer for Oriented Object Detection in Aerial Images
Jian Ding,Nan Xue,Yang Long,Gui-Song Xia,Qikai Lu +4 more
- 01 Jun 2019
TL;DR: The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations.
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Xue Yang,Jirui Yang,Junchi Yan,Yue Zhang,Tengfei Zhang,Zhi Guo,Xian Sun,Kun Fu +7 more
- 01 Oct 2019
TL;DR: A sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects, and the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box.
•Proceedings Article
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Xue Yang,Junchi Yan,Ziming Feng,Tao He +3 more
- 30 Nov 2019
TL;DR: Yang et al. as mentioned in this paper proposed an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity.
624
Arbitrary-Oriented Object Detection with Circular Smooth Label
Xue Yang,Junchi Yan +1 more
- 23 Aug 2020
TL;DR: This paper designs a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection.
613
•Posted Content
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
TL;DR: The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through feature interpolation to realize feature reconstruction and alignment.
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