An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

Sun, Zhongzhen; Dai, Maolin; Leng, Xiangguang; Yu, Lei; Xiong, Boli; Ji, Kefeng; Kuang, Gangyao

doi:10.1109/jstars.2021.3099483

Cited by 139 publications

(67 citation statements)

References 55 publications

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“…Geng et al [36] proposed a two-stage ship detection for land-contained sea. Sun et al [37] first applied the fully convolutional one-stage object detection (FCOS) network to detect ship targets in HR SAR images, and the proposed method can obtain encouraging detection performance on different datasets. Bao et al [38] designed an optical ship detector (OSD) pretraining technique and an optical-SAR matching (OSM) pretraining technique to boosting ship detection in SAR images.…”

Section: Deep Learning-based Horizontal Sar Ship Detection Methodsmentioning

confidence: 99%

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

et al. 2021

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Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.

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Section: Deep Learning-based Horizontal Sar Ship Detection Methodsmentioning

confidence: 99%

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

et al. 2021

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“…Zhang et al [38] proposed a part-based RCNN (Regional Convolutional Neural Network) by setting up component modules to detect the target class. In 2021, Sun et al [39] proposed an anchor-free SAR image-based ship target detection method, which was proven to have good ship performance through extensive experiments. Nie et al [40] proposed an improved Mask R-CNN model that can accurately detect and segment ships from remote sensing images at the pixel level.…”

Section: Related Workmentioning

confidence: 99%

Ship images detection and classification based on convolutional neural network with multiple feature regions

Sun

Huo

2022

IET Signal Processing

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In recent years, the maritime industry is developing rapidly, which poses great challenges for intelligent ship navigation systems to achieve accurate ship classification. To cope with this problem, a Recurrent Attention Convolutional Neural Network (RA‐CNN) is proposed, which is fused with multiple feature regions for ship classification. The proposed model has three scale layers, each of which contains a classification network VGG‐19 and a localisation head Attention Proposal Network (APN). First, the Scale Dependent Pooling algorithm is integrated with VGG‐19 to reduce the impact of over‐pooling and improve the classification performance of small ships. Second, the APN incorporates the Joint Clustering algorithm to generate multiple independent feature regions; thus, the whole model can make full use of the global information in ship recognition. In the meantime, the Feature Regions Optimisation method is designed to solve the overfitting problem and reduce the overlap rate of multiple feature regions. Finally, a novel loss function is defined to cross‐train VGG‐19 and APN, which accelerates the convergence process. The experimental results show that the classification accuracy of the authors’ proposed method reaches 90.2%, which has a 6% improvement over the baseline RA‐CNN. Both classification accuracy and robustness are improved by a large margin compared to those of other compared models.

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“…Airport detection tasks suffer from a quantitative imbalance between positive and negative samples, so RetinaNet is considered to be well suited as a benchmark method for SAD. In addition, RetinaNet has been widely used for various tasks after its proposal, including ship detection in SAR images [47], [48], multiscale objects detection [49], etc.…”

Section: A Retinanetmentioning

confidence: 99%