Ship Target Research Articles

Real-time tracking and dynamic berthing information extraction system with 2D LiDAR data

In smart ports, accurately perceiving the state of a berthing vessel relative to the berth is essential for ensuring the safety and efficiency of vessels and ports. This paper presents a real-time tracking and dynamic berthing information extraction system based on two-dimensional light detection and ranging (LiDAR). First, data preprocessing, fast segmentation, and filtering are performed in polar coordinates based on the continuous measurements and high angular resolution of the LiDAR. Moreover, the ship clusters are merged owing to “blind spot”. Then, simple online and real-time tracking with ship features is proposed by modifying the simple online and real-time tracking with a deep association metric framework to remove invalid frames and adapt to different berthing scenarios. Qualitative and quantitative analyses of the dynamic distance and approach angle berthing parameters relative to the dock are performed. The frequency of the calculation results can reach a rate of 20 Hz for LiDAR. On-site monitoring experiments were conducted on the training ship YuKun berthing/unberthing in the Dalian Port and a simulated berthing scenario was also established to verify the algorithm. The proposed method proved feasible and effective for dynamic ship target tracking and ensuring safe ship berthing.

A Novel Intelligent Ship Detection Method Based on Attention Mechanism Feature Enhancement

The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorithm is proposed. First of all, on the basis of YOLOv4, the convolutional block attention module (CBAM) is used to search for features in channel and space dimensions, respectively, to improve the model’s feature perception of ship targets. Then, the improved-efficient intersection over union (EIoU) loss function is used to replace the complete intersection over union (CIoU) loss function of the YOLOv4 algorithm to improve the algorithm’s perception of ships of different sizes. Finally, in the post-processing of algorithm prediction, soft non-maximum suppression (Soft-NMS) is used to replace the non-maximum suppression (NMS) of YOLOv4 to reduce the missed detection of overlapping ships without affecting the efficiency. The proposed method is verified on the large data set SeaShips, and the average accuracy rate of mAP0.5–0.95 reaches 72.5%, which is 10.7% higher than the original network YOLOv4, and the FPS is 38 frames/s, which effectively improves the ship detection accuracy while ensuring real-time performance.

Research on a Novel Combined Cooling and Power Scheme for LNG-Powered Ship

Cold energy recovery in LNG-powered vessels can not only improve the utilization efficiency of energy, but also benefit environmental protection. This paper put forward a new cascade scheme for utilizing flue gas waste heat and LNG cold energy comprehensively. The scheme was integrated by a dual organic Rankine circulation system (ORC), a high- and low-temperature cold storage system (TCS), an air conditioning system (ACS) and a seawater desalination system (SDS). The working medium of the dual ORC system was firstly determined by considering exergy efficiency and economic index simultaneously. On this basis, the adaptive weighted particle swarm optimization algorithm was employed to enhance thermodynamic performance of the scheme with the net output power as the optimization objective. The maximum net power and annual net interest rate can reach 725.78 kW and $115,300, respectively. Furthermore, the economic benefit of the scheme was analyzed by referring to the running track and the operation condition of the target ship. The results showed that the proposed scheme is a potential large-scale cryogenic technology and can bring considerable economic benefits to ship navigation.

Scattering-Point-Guided RPN for Oriented Ship Detection in SAR Images

Ship detection in synthetic aperture radar (SAR) images has attracted widespread attention due to its significance and challenges. In recent years, numerous detectors based on deep learning have achieved good performance in the field of SAR ship detection. However, ship targets of the same type always have various representations in SAR images under different imaging conditions, while different types of ships may have a high degree of similarity, which considerably complicates SAR target recognition. Meanwhile, the ship target in the SAR image is also obscured by background and noise. To address these issues, this paper proposes a novel oriented ship detection method in SAR images named SPG-OSD. First, we propose an oriented two-stage detection module based on the scattering characteristics. Second, to reduce false alarms and missing ships, we improve the performance of the network by incorporating SAR scattering characteristics in the first stage of the detector. A scattering-point-guided region proposal network (RPN) is designed to predict possible key scattering points and make the regression and classification stages of RPN increase attention to the vicinity of key scattering points and reduce attention to background and noise. Third, supervised contrastive learning is introduced to alleviate the problem of minute discrepancies among SAR object classes. Region-of-Interest (RoI) contrastive loss is proposed to enhance inter-class distinction and diminish intra-class variance. Extensive experiments are conducted on the SAR ship detection dataset from the Gaofen-3 satellite, and the experimental results demonstrate the effectiveness of SPG-OSD and show that our method achieves state-of-the-art performance.

Proposal of Zero-Emission Tug in South Korea Using Fuel Cell/Energy Storage System: Economic and Environmental Long-Term Impacts

This study presents the results of economic and environmental analysis for two types of zero-emission ships (ZESs) that are receiving more attention to meet strengthened environmental regulations. One of the two types of ZES is the ZES using only the energy storage system (All-ESS), and the other is the ZES with fuel cell and ESS hybrid system (FC–ESS). The target ship is a tug operating in South Korea, and the main parameters are based on the specific circumstances of South Korea. The optimal capacity of the ESS for each proposed system is determined using an optimization tool. The total cost for a ship’s lifetime is calculated using economic analysis. The greenhouse gas (GHG) emission for the fuel’s lifecycle (well-to-wake) is calculated using environmental analysis. The results reveal that the proposed ZESs are 1.7–3.4 times more expensive than the conventional marine gas oil (MGO)-fueled ship; however, it could be reduced by 1.3–2.4 times if the carbon price is considered. The proposed ZESs have 58.7–74.3% lower lifecycle GHG emissions than the one from the conventional ship. The results also highlight that the electricity- or hydrogen-based ZESs should reduce GHG emissions from the upstream phase (well-to-tank) to realize genuine ZESs.

Ship Detection in Nonhomogeneous Sea Clutter Based on Polarization-Time–Frequency Optimal Using Polarimetric SAR

Synthetic aperture radar (SAR) ship target detection under nonhomogeneous sea conditions is changeable. In this article, according to the characteristics of the target and ocean during SAR imaging, the polarization-time–frequency coherent optimal detector PTFO is constructed, and then the constant false alarm rate method is used to detect ship targets with a stable scattering in SAR images. Four quad-polarimetric RADARSAT-2 data are used to analyze the ship–clutter contrast enhancement capability of PTFO quantitatively, and the appropriate number of time–frequency decompositions is determined to be 3. The proposed method can obtain an FOM of 0.95, which is better than other classical methods to control the detection accuracy and suppress the appearance of false alarm targets.

Toward an Online Decision Support System to Improve Collision Risk Assessment at Sea

Increasing ship speeds and unpredictable environmental perturbations raise the difficulty of situation awareness and collision avoidance—for instance, when maneuvering in narrow and busy surroundings. Providing decision support for these operations automatically and timely is, thus, of great concern in terms of ship safety. To achieve it, we developed an online decision support system (DSS) to provide navigators with intuitive and reliable solutions, such as for collision risk and the point of collision, in real time. More specifically, the collision risk for each target ship (TS) is colored coded, and the ship domain is marked by a heat map, which can remind the navigators of the surrounding situations intuitively. Afterward, a comparative case study with and without decision support was conducted to verify the effectiveness of the DSS during the busy water navigation process around a high-fidelity simulator. Experiment results show that the collision risk can be significantly reduced with the help of the proposed DSS. It has the potential to be used onboard as a complementary service in the near future.

SARFB: Strengthened Asymmetric Receptive Field Block for Accurate Infrared Ship Detection

Convolutional neural network (CNN)-based detection has shown great potential in accurate infrared (IR) ship detection. Typically, IR images exhibit a lack of texture details, whereas the sizes of IR ship targets are extremely multiscale, making it difficult to accurately detect IR ship targets. Herein, we propose a novel strengthened asymmetric receptive field block (SARFB) for accurate IR ship detection. The SARFB contains an asymmetric receptive field block (ARFB), a spatial pyramid pooling (SPP) block, and skip connections. Through these components, SARFB is able to fuse local and global features, enriching the expressive ability and receptive field of the network for multiscale IR ship target detection. Furthermore, because there is no publicly available IR ship target dataset for detection, we created the single-frame IR ship detection (SFISD) dataset, providing the first public benchmark for testing IR ship target detection performance. In comparative studies, the mAP_0.5 of Yolov5 with SARFB reached 93.3%, outperforming other state-of-the-art methods. Finally, we performed experiments on an unmanned surface vehicle (USV) equipped with an IR camera. The results show the superior robustness of our proposed method, especially when target texture information is lacking, and when the IR ship targets are multiscale. The SFISD dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/echoo-sky/SFISD</uri> .

A Novel Bistatic SAR Maritime Ship Target Imaging Algorithm Based on Cubic Phase Time-Scaled Transformation

Due to the advantages of flexible configuration, bistatic synthetic aperture radar (BiSAR) has the ability to effectively observe from various visual angles, such as forward view area and squint area, and has good anti-jamming characteristics. It can be applied to the surveillance of ship targets on the sea and is gradually gaining an increasing amount of attention. However, for ship targets with complex motions on the sea surface, such as maneuvering targets or ship targets under high sea conditions, the high-order Doppler frequency of the scattering points is always spatial variation (related to the spatial position of scattering points), which poses a considerable challenge for the imaging of maritime ship targets in BiSAR. To resolve this problem, a BiSAR maritime ship target imaging algorithm based on cubic phase time-scaled transformation is proposed in this paper. First, through pre-processing of echo such as Doppler prefiltering and keystone transform, the translation compensation of the BiSAR maritime ship target is completed, and the scattering point energy is corrected to within one range unit. Then, the azimuth signal is modeled as a multi-component cubic phase signal. Based on the proposed cubic phase time-scaled transformation, the Doppler centroid, frequency rate, and third-order frequency of scattering points are estimated. Eventually, the BiSAR imaging of maritime ship targets is realized. This algorithm has excellent noise immunity and low cross-terms. The simulation leads to the verification of the validity of the proposed algorithm.

Complex encounter situation modeling and prediction method for unmanned ships based on bounded rational game

Comprehensive, accurate, and timely cognition of navigation situation is a prerequisite to make safe, efficient, and scientific decisions for unmanned ships. Previous research on this topic has been more focused on situation perception, and lacks studies of mathematical representation, modeling, and prediction for multi-ship complex encounter situation from an integrated perspective. Based on this, vectorized model is constructed to express the situation for the target ship in open waters. Through analysis of bounded rational dynamic game between ships, the navigation situation of the target ship can be predicted. Historical data of Automatic Identification System (AIS) both from public data set and experiments is used to verify the prediction method. The results show that the research can provide theoretical basis for studies on multi-ship encounter situation, and supply a reference for navigation decisions of unmanned ships.

Feature extraction method of ship-radiated noise based on dispersion entropy: A review

There is abundant ship information in ship-radiated noise, which is helpful for ship target recognition, classification and tracking. However, owing to the increasing complexity of the marine environment, it makes difficult to extract S-RN features. Dispersion entropy has been proven to be an excellent method to extract the features of S-RN by analyzing the complexity of S-RN, and has been widely used in feature extraction of S-RN. This paper summarizes the research progress of DE in the feature extraction of S-RN in recent years, and provides a comprehensive reference for researchers related to this topic. First, DE and its improved algorithm are described. Then the traditional and DE-based S-RN feature extraction methods are summarized, and the application of DE in S-RN feature extraction methods is concluded from two aspects: methods that apply DE algorithms only and methods that combine DE with mode decomposition algorithms. Finally, the research prospects of DE and the summary of this paper are given.

함정의 평판형 방향타 캐비테이션 침식에 대한 모형 시험 연구

In the present study, a method of performing cavitation erosion test directly on the anodized surface of the rudder model is proposed, not applying ink or paint on its surface. An image processing technique is newly developed to quantitatively evaluate the erosion damages on the rudder model surface after erosion test. The preprocessing saturation image, image smoothing, adaptive hysteresis thresholding and eroded area detection algorithms are in the image processing program. The rudder cavitation erosion tests are conducted in the rudder deflection angle range of 0SUPo/SUP to -4SUPo/SUP, which is used to maintain a straight course at the highest speed of the targeted navy ship. In the case of the conventional flat-type full-spade rudder currently being used in the target ship, surface erosion can occur on the model rudder surface in the above rudder deflection angle range. The bubble type of cavitation occurs on rudder surface, which is estimated to be the main reason of erosion damage on the rudder surface.

SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research.

The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models to have high confidence in output errors. There are potential risks in a system, and input data that contain confrontation samples can be easily used by malicious people to attack the system. For a safe and stable model, attack algorithms need to be studied. The goal of traditional attack algorithms is to destroy models. When defending against attack samples, a system does not consider the generalization ability of the model. Therefore, this paper introduces an attack algorithm which can improve the generalization of models by based on the attributes of Gaussian noise, which is widespread in actual SAR systems. The attack data generated by this method have a strong effect on SAR ship detection models and can greatly reduce the accuracy of ship recognition models. While defending against attacks, filtering attack data can effectively improve the model defence capabilities. Defence training greatly improves the anti-attack capacity, and the generalization capacity of the model is improved accordingly.

Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence

Optical remote sensing ship target detection has become an essential means of ocean supervision, coastal defense, and frontier defense. Accurate, effective, fast, and real-time remote sensing data processing is the critical technology in this field. This paper proposes a real-time detection algorithm for moving targets in low-resolution wide-area remote sensing images, which includes four steps: pre-screening, simplified HOG feature identification, sequence correlation identification, and facilitated Yolo identification. It can effectively detect and track targets in low-resolution sequence data. Firstly, iterative morphological processing was used to improve the contrast of low-resolution ship target profile edge features compared with the sea surface background. Next, the target area after adaptive segmentation was used to eliminate false alarms. As a result, the invalid background information of extensive comprehensive data was quickly eliminated. Then, support vector machine classification of S-HOG feature was carried out for suspected targets, and interference such as islands and reefs, broken clouds, and waves were eliminated according to the shape characteristics of ship targets. The method of multi-frame data association and searching for adjacent target information between frames was adopted to eliminate the interference of static targets and broken clouds with similar contours. Finally, the sequential marks were further trained and learned, and further false alarm elimination was completed based on the clipped Yolo network. Compared with the traditional Yolo Tiny V2/V3 series network, this method had higher computational speed and better detection performance. The F1 number of detection results was increased by 3%, and the calculation time was reduced by 66%.

Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images

ABSTRACT As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects.

Infrared ship target segmentation based on Adversarial Domain Adaptation

Infrared ship target segmentation is one of the key technologies for automatically detecting ship targets in ocean monitoring. However, it is a challenging work to achieve accurate target segmentation from the infrared ship image. To improve its segmentation performance, we present an Adversarial Domain Adaptation Network (ADANet) for infrared ship target segmentation, where the labeled visible ship images are used as the source domain and the unlabeled infrared ship images are as the target domain. To address the issue of style difference between the two domains, we preprocess the visible images of the source domain in turn with graying and whitening to convert them into the images with the style of the target domain. For the infrared images in the target domain, we optimize them with a denoising network. Furthermore, to solve the matter of limited receptive field of the discriminator, we design a discriminator based on atrous convolution to improve its discriminative ability. Finally, for the issue of low confidence of the target domain predicted images, we add the information entropy of the target domain predicted images to the adversarial loss. Experimental results on the home-made dataset as well as a public dataset show that infrared ship target segmentation achieves higher mean intersection over union than the state-of-the-art methods without significantly increase of parameters, demonstrating its effectiveness.

Automatic Ship Object Detection Model Based on YOLOv4 with Transformer Mechanism in Remote Sensing Images

Despite significant advancements in object detection technology, most existing detection networks fail to investigate global aspects while extracting features from the inputs and cannot automatically adjust based on the characteristics of the inputs. The present study addresses this problem by proposing a detection network consisting of three stages: preattention, attention, and prediction. In the preattention stage, the network framework is automatically selected based on the features of the images’ objects. In the attention stage, the transformer structure is introduced. Taking into account the global features of the target, this study combines a self-attention module in the transformer model and convolution operation to integrate image features from global to local and for detection, thus improving the ship target accuracy. This model uses mathematical methods to obtain results of predictive testing in the prediction stage. The above improvements are based on the You Only Look Once version 4 (YOLOv4) framework, named “Auto-T-YOLO”. The model achieves the highest accuracy of 96.3% on the SAR Ship Detection dataset (SSDD) compared to the other state-of-the-art (SOTA) model. It achieves 98.33% and 91.78% accuracy in the offshore and inshore scenes, respectively. The experimental results verify the practicality, validity, and robustness of the proposed model.

Statistical Adaptation Loss Improved SMALL Sample Ship Detection Method Based on an Attention Mechanism and Data Enhancement

Synthetic aperture radar (SAR) imagery is a promising data source for ocean activity detection. Ship target detection based on SAR images is widely used in maritime trade and the military. SAR image data are rare, and the amount of public data is small. For applications of SAR image ship target detection, a model with low data dependence, fast iteration and low training cost is needed. In this paper, the balanced positive and negative data enhancement method was used. Through statistical analysis of the training dataset, similar sea areas in the training set are filled with detection targets with comfortable size features. Increasing the proportion of positive samples in the data helps to improve the model detection effect. The regional attention preadaptation mechanism based on statistical analysis was implemented to extract information, and the scale-adaptive loss was combined to improve the detection accuracy of the model. Using the same data, our model exhibited better performance. When using 30% of the data, our model was stable in terms of accuracy and average precision (AP) and maintained detection results similar to the training results achieved using 100% of the dataset.

Path Planning for Ferry Crossing Inland Waterways Based on Deep Reinforcement Learning

Path planning is a key issue for safe navigation of inland ferries. With the development of ship intelligence, how to enhance the decision–support system of a ferry in a complex navigation environment is one of the key issues. The inland ferries need to cross the channel frequently and, thus, risky encounters with target ships in the waterway are more frequent, so they need an intelligent decision–support system that can deal with complex situations. In this study, a reinforced deep learning method is proposed for path planning of inland ferries during crossing of the waterways. In the study, the state space, action space and reward function of the Deep Q-network (DQN) model are designed and improved to establish an autonomous navigation method for ferries considering both economy and safety. The DQN model also takes into account the crossing behavior, navigation economy and safety. Finally, the model is applied to case studies to verify its effectiveness.

SAR image near-shore ship target detection method in complex background

ABSTRACT Due to background clutter in synthetic aperture radar (SAR) images, the detection of dense ship targets suffers from a low detection rate, high false alarm rate, and high missed detection rate. To address this issue, an FSM-DFF-YOLOv5+Confluence algorithm is proposed in this paper for the detection of near-shore ship targets in SAR images with complex backgrounds. First, based on the YOLOv5 target detection algorithm, two improvements are made in the feature extraction network: feature refinement and multi-feature fusion; in the feature extraction network, deformable convolutional neural networks are adopted to change the position of the target sampling points of the convolution to improve the feature extraction capability of the target and the detection rate of ship targets in SAR images with a complex background; in the multi-feature fusion network structure, cascading and parallel pyramids are used in the multi-feature fusion network to realize feature fusion at different levels; the visual perceptual field of feature extraction is expanded by using null convolution to enhance the adaptability of the network to detect near-shore multi-scale ship targets with complex backgrounds and reduce the false alarm rate of ship target detection in SAR images with complex environments. In this way, the DFF-YOLOv5 near-shore ship target detection algorithm is established. Meanwhile, to address the problem of missed detection in near-shore dense ship target detection, this paper adds rectangular convolution kernels to the convolution of the feature extraction network to better realize the feature extraction of dense ship targets in SAR images with complex backgrounds. Besides, the Confluence algorithm instead of non-maximum suppression is used in the prediction stage. Through experiments on the constructed complex background near-shore ship detection dataset, it is indicated that the average accuracy of the FSM-DFF-YOLOv5+Confluence detection algorithm reaches 88.96%, and the recall rate reaches 88.80%.