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Open AccessArticle

R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images

Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China
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Remote Sens. 2020, 12(12), 2031; https://doi.org/10.3390/rs12122031
Received: 26 May 2020 / Revised: 14 June 2020 / Accepted: 20 June 2020 / Published: 24 June 2020
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in various scenarios, which makes it difficult to exclude the disruption of the cluttered background. Second, it becomes more complicated to precisely locate the targets with large aspect ratios, arbitrary orientations and dense distributions. Third, the trade-off between accurate localization and improved detection efficiency needs to be considered. To address these issues, this paper presents a rotate refined feature alignment detector (R 2 FA-Det), which ingeniously balances the quality of bounding box prediction and the high speed of the single-stage framework. Specifically, first, we devise a lightweight non-local attention module and embed it into the stem network. The recalibration of features not only strengthens the object-related features yet adequately suppresses the background interference. In addition, both forms of anchors are integrated into our modified anchor mechanism and thus can enable better representation of densely arranged targets with less computation burden. Furthermore, considering the shortcoming of the feature misalignment existing in the cascaded refinement scheme, a feature-guided alignment module which encodes both the position and shape information of current refined anchors into the feature points is adopted. Extensive experimental validations on two SAR ship datasets are performed and the results demonstrate that our algorithm has higher accuracy with faster speed than some state-of-the-art methods. View Full-Text
Keywords: attention module; cascade architecture; feature guided alignment module; modified anchor mechanism; single-stage detector; synthetic aperture radar (SAR) ship detection attention module; cascade architecture; feature guided alignment module; modified anchor mechanism; single-stage detector; synthetic aperture radar (SAR) ship detection
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MDPI and ACS Style

Chen, S.; Zhang, J.; Zhan, R. R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images. Remote Sens. 2020, 12, 2031. https://doi.org/10.3390/rs12122031

AMA Style

Chen S, Zhang J, Zhan R. R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images. Remote Sensing. 2020; 12(12):2031. https://doi.org/10.3390/rs12122031

Chicago/Turabian Style

Chen, Shiqi; Zhang, Jun; Zhan, Ronghui. 2020. "R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images" Remote Sens. 12, no. 12: 2031. https://doi.org/10.3390/rs12122031

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