A High-Effective Implementation of Ship Detector for SAR Images

Gao, Shengli; Liu, J. M.; Miao, Yinxiao; He, Zhijian

doi:10.1109/lgrs.2021.3115121

Cited by 29 publications

(9 citation statements)

References 22 publications

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“…The LS-SSDD-v1.0 dataset is widely used for SAR image intelligent interpretation [51][52][53][54]. The characteristic of small ships with large-scale backgrounds in LS-SSDD is close to actual satellite images; thus, we adopted the LS-SSDD-v1.0 dataset to verify the effectiveness of Lite-YOLOv5.…”

Section: Datasetmentioning

confidence: 99%

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Zhang

2022

Remote Sensing

155

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Synthetic aperture radar (SAR) satellites can provide microwave remote sensing images without weather and light constraints, so they are widely applied in the maritime monitoring field. Current SAR ship detection methods based on deep learning (DL) are difficult to deploy on satellites, because these methods usually have complex models and huge calculations. To solve this problem, based on the You Only Look Once version 5 (YOLOv5) algorithm, we propose a lightweight on-board SAR ship detector called Lite-YOLOv5, which (1) reduces the model volume; (2) decreases the floating-point operations (FLOPs); and (3) realizes the on-board ship detection without sacrificing accuracy. First, in order to obtain a lightweight network, we design a lightweight cross stage partial (L-CSP) module to reduce the amount of calculation and we apply network pruning for a more compact detector. Then, in order to ensure the excellent detection performance, we integrate a histogram-based pure backgrounds classification (HPBC) module, a shape distance clustering (SDC) module, a channel and spatial attention (CSA) module, and a hybrid spatial pyramid pooling (H-SPP) module to improve detection performance. To evaluate the on-board SAR ship detection ability of Lite-YOLOv5, we also transplant it to the embedded platform NVIDIA Jetson TX2. Experimental results on the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) show that Lite-YOLOv5 can realize lightweight architecture with a 2.38 M model volume (14.18% of model size of YOLOv5), on-board ship detection with a low computation cost (26.59% of FLOPs of YOLOv5), and superior detection accuracy (1.51% F1 improvement compared with YOLOv5).

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Section: Datasetmentioning

confidence: 99%

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Zhang

2022

Remote Sensing

155

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“…Sun et al [10] proposed a model based on a densely connected deep neural network with an attention mechanism (Dense-YOLOv4-CBAM) to enhance the transmission of image features. Liu et al [11] carried out work based on YOLOv4, through feature pyramid network (FPN) [5], to obtain multi-scale semantic information and use scale-equalizing pyramid convolution (SEPC) to balance the correlation of multi-scale semantic information, and proposed SAR-Net. Wang et al [12] added multi-scale convolution and a transformer module to YOLO-X to improve the performance of YOLO-X in detecting ships.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7

Tang,

Gao,

et al. 2024

Remote Sensing

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The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy (AP50) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively.

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“…The representative algorithms of single-stage methods are You Only Look Once (YOLO) [14], RetinaNet [15] and Single Shot Detection (SSD) [16]. In order to make YOLOv4 network [17] more suitable for ship detection in SAR images, Gao et al [18] introduced scale-equalizing pyramid convolution module and convolutional block attention module into the network, and modified the head of the YOLOv4. Represented by Faster R-CNN [19], the two-stage method with the Region Proposal Network (RPN) trades speed for an increase in detection precision.…”

Section: Introductionmentioning

confidence: 99%

A Generating-Anchor Network for Small Ship Detection in SAR Images

Yue

Zhang

Liu

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) ship detection especially for small ships has issues such as dense distribution of ships, interference from land and small islands. To address these issues, many deep learning (DL) methods, including anchor-based and anchor-free methods, have been successfully migrated from optical scenes to SAR images. However, when the preset scale of anchors does not match well with the ships, it will seriously reduce the detection precision. Due to the lack of anchor-based refinement process, anchor-free methods may generate missing or false alarms in complex scenarios. In this paper, a two-stage ship detection network which can generate anchors is proposed. Firstly, our method generates high-quality anchors by network, which is more beneficial for the network to capture small ships. In addition, the generated anchors are centrally set in the region of ships, which reduces the number of anchors unrelated to ships. Secondly, the receptive field enhancement module is inserted into the feature pyramid network (FPN). It sets different dilation ratios of atrous convolution according to the scale of the feature map, which further enriches the semantic information of the elements in the feature map. Therefore, the network can use the information of a wider region effectively to detect ships. Finally, to verify the effectiveness of our method, extensive experiments are carried out on SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID). The results show that our method has more strong ability of detecting small ships, and achieves better detection performance than some state-of-the-art methods.

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A High-Effective Implementation of Ship Detector for SAR Images

Cited by 29 publications

References 22 publications

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7

A Generating-Anchor Network for Small Ship Detection in SAR Images

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