Ship Detection on Single-Band Grayscale Imagery Using Deep Learning and AIS Signal Matching Using Non-Rigid Transformations

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

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Probabilistic Ship Detection and Classification Using Deep Learning

Applied Sciences ◽

10.3390/app8060936 ◽

2018 ◽

Vol 8 (6) ◽

pp. 936 ◽

Cited By ~ 6

Author(s):

Kwanghyun Kim ◽

Sungjun Hong ◽

Baehoon Choi ◽

Euntai Kim

Keyword(s):

Deep Learning ◽

Ship Detection

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CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection

Remote Sensing ◽

10.3390/rs13112208 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2208

Author(s):

Yi Yang ◽

Zongxu Pan ◽

Yuxin Hu ◽

Chibiao Ding

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Positive Sample ◽

Feature Points ◽

Annotation Scheme ◽

Ship Detection ◽

Learning Network ◽

Public Dataset ◽

Deep Learning Network ◽

Speed And Accuracy

Ship detection is a significant and challenging task in remote sensing. At present, due to the faster speed and higher accuracy, the deep learning method has been widely applied in the field of ship detection. In ship detection, targets usually have the characteristics of arbitrary-oriented property and large aspect ratio. In order to take full advantage of these features to improve speed and accuracy on the base of deep learning methods, this article proposes an anchor-free method, which is referred as CPS-Det, on ship detection using rotatable bounding box. The main improvements of CPS-Det as well as the contributions of this article are as follows. First, an anchor-free based deep learning network was used to improve speed with fewer parameters. Second, an annotation method of oblique rectangular frame is proposed, which solves the problem that periodic angle and bounded coordinates in conjunction with the regression calculation can lead to the problem of loss anomalies. For the annotation scheme proposed in this paper, a scheme for calculating Angle Loss is proposed, which makes the loss function of angle near the boundary value more accurate and greatly improves the accuracy of angle prediction. Third, the centerness calculation of feature points is optimized in this article so that the center weight distribution of each point is suitable for the rotation detection. Finally, a scheme combining centerness and positive sample screening is proposed and its effectiveness in ship detection is proved. Experiments on remote sensing public dataset HRSC2016 show the effectiveness of our approach.

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Ship Detection in Medium-Resolution SAR Images using Deep learning

International Journal of Electronics and Communication Engineering ◽

10.14445/23488549/ijece-v8i5p101 ◽

2021 ◽

Vol 8 (5) ◽

pp. 1-5

Author(s):

Muruga Lakshmi M ◽

Thayammal S

Keyword(s):

Deep Learning ◽

Sar Images ◽

Ship Detection ◽

Medium Resolution

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Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning

ITM Web of Conferences ◽

10.1051/itmconf/20171205012 ◽

2017 ◽

Vol 12 ◽

pp. 05012 ◽

Cited By ~ 7

Author(s):

Ying Liu ◽

Hong-Yuan Cui ◽

Zheng Kuang ◽

Guo-Qing Li

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection

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Ship Detection from Remote Sensing Images Based on Deep Learning

Communications in Computer and Information Science - Geo-Spatial Knowledge and Intelligence ◽

10.1007/978-981-13-0893-2_36 ◽

2018 ◽

pp. 336-344

Author(s):

Ziqiang Yuan ◽

Jing Geng ◽

Tianru Dai

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Remote Sensing Images ◽

Ship Detection

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Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model

Applied Sciences ◽

10.3390/app10217751 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7751

Author(s):

Seong-Jae Hong ◽

Won-Kyung Baek ◽

Hyung-Sup Jung

Keyword(s):

Deep Learning ◽

Learning Process ◽

Intensity Difference ◽

Sar Images ◽

Ship Detection ◽

Detection Model ◽

Learning Techniques ◽

Vessel Detection ◽

Intensity Images ◽

Deep Learning Model

Synthetic aperture radar (SAR) images have been used in many studies for ship detection because they can be captured without being affected by time and weather. In recent years, the development of deep learning techniques has facilitated studies on ship detection in SAR images using deep learning techniques. However, because the noise from SAR images can negatively affect the learning of the deep learning model, it is necessary to reduce the noise through preprocessing. In this study, deep learning vessel detection was performed using preprocessed SAR images, and the effects of the preprocessing of the images on deep learning vessel detection were compared and analyzed. Through the preprocessing of SAR images, (1) intensity images, (2) decibel images, and (3) intensity difference and texture images were generated. The M2Det object detection model was used for the deep learning process and preprocessed SAR images. After the object detection model was trained, ship detection was performed using test images. The test results are presented in terms of precision, recall, and average precision (AP), which were 93.18%, 91.11%, and 89.78% for the intensity images, respectively, 94.16%, 94.16%, and 92.34% for the decibel images, respectively, and 97.40%, 94.94%, and 95.55% for the intensity difference and texture images, respectively. From the results, it can be found that the preprocessing of the SAR images can facilitate the deep learning process and improve the ship detection performance. The results of this study are expected to contribute to the development of deep learning-based ship detection techniques in SAR images in the future.

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