A Multilayer Fusion Light-Head Detector for SAR Ship Detection

Gui, Yunchuan; Li, Xiuhe; Xue, Lei

doi:10.3390/s19051124

Cited by 29 publications

(9 citation statements)

References 33 publications

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“…SAR images in this dataset possess different satellite sensors, various polarization modes, multiple resolutions, different scenes, and abundant ship sizes, so it can verify the robustness of methods. Therefore, many scholars [10,21,35,[55][56][57][58][59][60][61][62][63] conducted research based on it for a better comparison.…”

Section: Datasetmentioning

confidence: 99%

“…We also compared our methods with some previous open studies which use the same SSDD dataset. To be clear, here, we replaced NVIDIA RTX2080Ti GPU with NVIDIA GTX1080 GPU to keep the hardware environment basically the same as previous other open studies (References [10,21,35,[59][60][61][62] used NVIDIA GTX1080 GPU.). For different performances of different types of GPUs, we have to make such a replacement in order to consider the rationality of the comparison experiment.…”

Section: Compared With Referencesmentioning

confidence: 99%

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Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

et al. 2019

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As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weather and all-time working capacity. Especially, in the marine field, SAR can provide numerous high-quality services for fishery management, traffic control, sea-ice monitoring, marine environmental protection, etc. Among them, ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation. Nowadays, most researches are focusing on improving the ship detection accuracy, while the detection speed is frequently neglected, regardless of traditional feature extraction methods or modern deep learning (DL) methods. However, the high-speed SAR ship detection is of great practical value, because it can provide real-time maritime disaster rescue and emergency military planning. Therefore, in order to address this problem, we proposed a novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN). In this approach, we integrated multi-scale detection mechanism, concatenation mechanism and anchor box mechanism to establish a brand-new light-weight network architecture for the high-speed SAR ship detection. We used DS-CNN, which consists of a depthwise convolution (D-Conv2D) and a pointwise convolution (P-Conv2D), to substitute for the conventional convolution neural network (C-CNN). In this way, the number of network parameters gets obviously decreased, and the ship detection speed gets dramatically improved. We experimented on an open SAR ship detection dataset (SSDD) to validate the correctness and feasibility of the proposed method. To verify the strong migration capacity of our method, we also carried out actual ship detection on a wide-region large-size Sentinel-1 SAR image. Ultimately, under the same hardware platform with NVIDIA RTX2080Ti GPU, the experimental results indicated that the ship detection speed of our proposed method is faster than other methods, meanwhile the detection accuracy is only lightly sacrificed compared with the state-of-art object detectors. Our method has great application value in real-time maritime disaster rescue and emergency military planning.

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Section: Datasetmentioning

confidence: 99%

Section: Compared With Referencesmentioning

confidence: 99%

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

et al. 2019

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“…Kang et al [12] combined Faster-RCNN with CFAR algorithm to improve the detection performance. Gui et al [13] designed a multi-layer fusion light-head network, which used the fusion features of shallow high-resolution and deep semantic feature to generate region proposals. Zhao et al [14] proposed an exhaustive ship proposal network and an accurate ship discrimination network for solving dense small ship detection in SAR images.…”

Section: Realted Workmentioning

confidence: 99%

A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images

Zhang

et al. 2020

IEEE Access

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Ship detection of synthetic aperture radar (SAR) images is one of the research hotspots in the field of marine surveillance. Fusing salient features to detection network can effectively improve the precision of ship detection. However, how to effectively fuse the salient features of SAR images is still a difficult task. In this paper, to improve the ship detection precision, we design a novel one-stage ship detection network to fuse salient features and deep convolutional neural network (CNN) features. Firstly, a saliency map extraction algorithm is proposed. The algorithm is applied to generate saliency map by using multi-scale pyramid features and frequency domain features. Secondly, the backbone of the ship detection network contains a two-stream network. The upper-stream network uses the original SAR image as input to extract multi-scale deep CNN features. The lower-stream network uses the corresponding saliency map as input to acquire multi-scale salient features. Thirdly, for integrating the salient features to deep CNN features, a novel salient feature fusion method is designed. Finally, an improved bi-directional feature pyramid network is applied to the ship detection network for reducing the computational complexity and network parameters. The proposed methods are evaluated on the public ship detection dataset and the experimental results shows that it can make a significant improvement in the precision of SAR image ship detection. INDEX TERMS Ship detection, synthetic aperture radar images, feature fusion, saliency map, deep convolutional neural network.

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“…Jiao et al [35] designed a multiscale CNN-based ship detector with dense connections based on the Faster R-CNN framework and the deeper ResNet-101 and added a feature fusion module between layers with different receptive fields to enhance representations of small, inshore, and offshore ships. Gui et al [36] embedded a feature fusion layer between the second and the fourth residual modules. In addition, these authors designed a light detection subnet based on separable convolution and position-sensitive RoI (PSRoI) and took the focal loss [37] as the loss function to help train the detector.…”

Section: Related Workmentioning

confidence: 99%

Multi-Size Convolution and Learning Deep Network for SAR Ship Detection From Scratch

Long

et al. 2020

IEEE Access

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Synthetic aperture radar (SAR) ship detection is a popular branch of SAR interpretation. A growing number of scholars are devoting themselves to applying convolutional neural network (CNN) to SAR ship detection. Currently, most CNN-based SAR ship detectors are variants of object detectors in optical images; however, the essential differences between SAR and optical images restrict their performance. To this end, by focusing on the attribute of SAR image's "point" property which is determined by its imaging mechanism, we design a novel SAR ship detector from scratch. The innovatively designed PCB-MSK (parallel convolutional block of multi-size kernels) consists of two groups of convolutions, each group is composed of four convolutional layers corresponding to kernel sizes of 3, 5, 7, and 9; the stride is 1 for one group and 2 for another. In the designed convolutional module with features reused (CMFR), the output and input feature maps of the previous block are concatenated for current layer to reduce information loss during forward propagation and to strengthen the supervision for shallow layers during parameter optimization. For each source prediction layer, the binary classification is first conducted to alleviate the positive/negative imbalance; deconvolution and feature fusion are utilized to enhance the feature representation. Then, we perform fine detection. Experiments on RDISD-SAR and SSDD, in which RDISD-SAR is meticulously constructed by us based on two open-access datasets, show that our method achieves a state-of-the-art accuracy and competitive speed, the average precision (AP) reaches 88.70% and 90.57% for RDISD-SAR and SSDD, respectively. These APs are 10.43% and 4.23% higher than DSOD, and 6.64% and 1.70% higher than ScratchDet. The detection speed is 58.2 FPS on a GTX 1080Ti GPU, the number of parameters is 18.19M and the amount of computations is 21.33G. In addition, experimental results show that the robustness of our detector is very excellent.

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A Multilayer Fusion Light-Head Detector for SAR Ship Detection

Cited by 29 publications

References 33 publications

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images

Multi-Size Convolution and Learning Deep Network for SAR Ship Detection From Scratch

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