Unmanned Ship Identification Based on Improved YOLOv8s Algorithm

Aiming at defects such as low contrast in infrared ship images,uneven distribution of ship size,and lack of texture details,which will lead to unmanned ship leakage misdetection and slow detection,this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm(R_YOLO).The algorithm incorporates the Efficient Multi-Scale Attention mechanism(EMA),the efficient Reparameterized Generalized-feature extraction module(CSPStage),the small target detection header,the Repulsion Loss function,and the context aggregation block(CABlock),which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference.The algorithm is validated in detail on two vessel datasets.The comprehensive experimental results demonstrate that,in the infrared dataset,the YOLOv8s algorithm exhibits improvements in various performance metrics.Specifically,compared to the baseline algorithm,there is a 3.1%increase in mean average precision at a threshold of 0.5(mAP(0.5)),a 5.4%increase in recall rate,and a 2.2%increase in mAP(0.5:0.95).Simultaneously,while less than 5 times parameters,the mAP(0.5)and frames per second(FPS)exhibit an increase of 1.7%and more than 3 times,respectively,compared to the CAA_YOLO algorithm.Finally,the evaluation indexes on the visible light data set have shown an average improvement of 4.5%.

Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

A Time-Domain Numerical Simulation for Free Motion Responses of Two Ships Advancing in Head Waves

The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems within a time domain framework,the free water surface needs to simultaneously satisfy both the kinematic and dynamic boundary conditions of the free water surface.This provides conditions for adding artificial damping layers.Using the Runge−Kutta method to solve equations related to time.An upwind differential scheme is used in the present method to deal with the convection terms on the free surface to prevent waves upstream.Through the comparison with the available experimental data and other numerical methods,the present method is proved to have good mesh convergence,and satisfactory results can be obtained.The constant panel method is applied to calculate the hydrodynamic interaction responses of two parallel ships advancing in head waves.Numerical simulations are conducted on the effects of forward speed,different longitudinal and lateral distances on the motion response of two modified Wigley ships in head waves.Then further investigations are conducted on the effects of different ship types on the motion response.

Structural Characteristics and Evolution of a Weighted Sino-US Container Shipping Network

This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural fea-tures.The network’s evolution is examined from three perspectives,namely,time,space,and event influence,aiming to comprehens-ively explore the network’s evolution mechanism.The results revealed that:1)the weighted Sino-US container shipping network exhib-its small-world and scale-free properties.Key hub ports in the United States include NEW YORK NY,SAVANNAH GA,LOS ANGELES CA,and OAKLAND CA,whereas SHANGHAI serving as the hub port in China.The geographical distribution of these hub ports is uneven.2)Concerning the evolution of the weighted Sino-US container shipping network,from a temporal perspective,the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and de-velopment,with a need for further enhancement in competitiveness and development speed.The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage.The shipping development in Northern China,the Western and Eastern United States,and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances.From a spatial perspective,the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani(BBV)model are confirmed through theoretical derivation.The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network.From an event impact per-spective,the Corona Virus Disease 2019(COVID-19)pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity.The network lacks sufficient resilience and stability in emergency situations.3)Based on the analysis of the structural characteristics and evolution of the weighted Sino-US con-tainer shipping network,recommendations for network development are proposed from three aspects:emphasizing the development of hub ports,focusing on the balanced development of the network,and optimizing the layout of Chinese ports.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Decoding RoPax Ship Capsizes and Development of an ISO Ship Safety Standard

Marine accidents often result in significant losses of human life, environmental damage, and property destruction. Additionally, ships and offshore plants are large-scale and complex systems, making safety assessments challenging. However, the advent of onboard electronic systems has made it possible to monitor and respond more effectively. These new technologies can enhance safety levels while reducing the workload on crews. In this paper, authors analyze recent accidents involving ships with high structures above the water, such as car carriers or RoPax vessels, and propose preventive safety indicators to help prevent similar accidents from recurring.

Sending The King Ship, A Tradition Connecting Two Peoples

“The sky is dark,and it is about to rain,”goes a lyric from China’s coastal Minnan(southern Fujian)region.“The king ship is leaving the bay,papa is going out to sea,and mama is sending the ship off.May it bring us wealth,food,and the gods’protection.”The 600-year-old custom is called Ong Chun,Wangchuan,Wangkang,or“Sending the King Ship.”

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

欧盟AUTOSHIP项目

欧洲水域自主航运倡议项目(Autonomous Shipping Initiative for European Waters,AUTOSHIP项目)由意大利Ciaotech S.r.l.公司牵头,联合康士伯等其他欧洲国家合作伙伴共同完成。该项目通过在不同环境下运营的两艘不同类型船舶上安装和测试自主航行设施,加速新一代自主航行船舶发展,并为欧盟实现船舶自主航行制定商业化路线图。

Corporate Social Responsibility and Shipping Supply Chain Risks

This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 2021.This study conclude that the more responsible companies have higher returns and are less affected by this event than the less responsible companies;the less responsible companies have lower returns.The companies with better CSR have a lower impact on their supply chains when faced with disruptions in the supply chain.

MMShip:中分辨率多光谱卫星图像船舶数据集

针对现有遥感船舶数据集均为裁剪后的图像,用数据集训练的检测算法直接运用于卫星图像原始尺度时检测效果较差的问题,建立了可见光和近红外4个波段的多光谱卫星船舶数据集MMShip,数据集同时包含卫星图像的原始尺度数据和切割后的小尺度船舶数据。本数据集引入多波段信息,弥补现有数据集多为可见光图像,而可见光容易受到光照条件等影响的缺点。在全球海域内下载云量低于3的Sentinel-2卫星图像,进行大气校正后只选取10 m分辨率的红绿蓝和近红外4个波段,以景为单位筛选出包含有船舶的图像。把筛选后的图像按无重叠的方式切分为512×512,剔除其中不包含船舶目标的图像。然后,使用LabelImage软件对小尺度数据进行了水平框标注,再将标注数据反推至原始尺度得到原始尺度下的标注信息。最后,利用几种典型的检测算法在切割后的MMShip小尺度数据集上进行了可见光、近红外、多光谱对比实验。构建了一个涵盖不同场景的多光谱卫星船舶目标数据集,包含497景原始尺度标注数据和裁剪后的5 016组船舶目标图像。对比实验验证了近红外波段信息的补充有助于提高船舶目标检测算法的精度。多光谱船舶数据集MMShip可用于卫星图像尺度和普通图像尺度的多光谱船舶目标检测算法研究。

Reliability Analysis Method of a Control System for Subsea All-Electric Christmas Tree

The subsea all-electric Christmas tree(XT) is a key equipment in subsea production systems.Once it fails,the marine environment will be seriously polluted.Therefore,strict reliability analysis and measures to improve reliability must be performed before a subsea all-electric XT is launched;such measures are crucial to subsea safe production.A fault-tolerant control system was developed in this paper to improve the reliability of XT.A dual-factor degradation model for electrical control system components was proposed to improve the evaluation accuracy,and the reliability of the control system was analyzed based on the Markov model.The influences of the common cause failure and the failure rate in key components on the reliability and availability of the control system were studied.The impacts of mean time to repair and incomplete repair strategy on the availability of the control system were also investigated.Research results show the key factors that affect system reliability,and a specific method to improve the reliability and availability of the control system was given.This reliability analysis method for the control system could be applied to general all-electric subsea control systems to guide their safe production.

Ship Detection and Recognition Based on Improved YOLOv7

In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of ships.The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset.This paper also introduces a novel multi-scale feature fusion module,which comprises Path Aggregation Network(PAN)modules,enabling the efficient capture of ship features across different scales.Furthermore,data preprocessing is enhanced through the application of data augmentation techniques,including random rotation,scaling,and cropping,which serve to bolster data diversity and robustness.The distribution of positive and negative samples in the dataset is balanced using random sampling,ensuring a more accurate representation of real-world scenarios.Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness,highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.

Adjusting the Parameters of PI Regulator in All-electrical Tank Gun Control System by Computer Program

Both the traverse subsystem and the elevation subsystem of the all-electrical tank gun control system are composed of electrical drive control system respectively. The parameters of PI regulator in these electrical drive control systems affect the performance of the control system seriously. Up to now, there is not a simple and practical method for choosing regulator parameters, which are usually determined by repeated and continual readjustment. This method is low efficient, and the parameters got are not always optimal. A method for on-line adjusting the parameters of PI regulator in the electrical drive control system by computer program is introduced in this paper. The function of adjusting PI parameters of the electrical drive control system is realized by PC program written by VC++ and controlling program written by assemble language and by the communication between PC and DSP completed by the control MSCOMM in VC++6.0. The method as mentioned above which is applied for an all-electrical tank gun control system under development is proved very available, a better performance might be obtained for the all-electrical tank gun control system easily.

All-Electric Aircraft Nose Wheel Steering System with Two Worm Gears

As all-electric aircraft has many advantages,an aircraft nose wheel steering system would be developed to the all-electric direction.Concerning the control demand of the nose wheel steering system,based on the basic principles of nose wheel steering system and the design technique of mechanotronics,an all-electric aircraft nose wheel steering system,composed of a nose wheel steering mechanism of two worm gear and a control servo system of fly-by-wire with both steering and anti-shimmy functions is designed to meet the demand for operation control in the nose wheel steering system.Then,based on the LMS-AMESim software,the simulation model of the system is established to simulate the dynamics for the verification of its steering function.The simulation results indicate that the nose wheel steering system is reasonable,and can meet the requirements of the general project.Furthermore,the prototypes of the steering mechanism and control system are studied to validate the design,and the steering test bench is prepared to test the designed system.The test results,such as steer angle,rotate speed of motor are analyzed in details and compared with the theoretical results.The analysis and comparison results show that the design is reasonable and the property of the prototype can achieve the design objectives.

Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale representation,sparsity,and severe background interference,which make the existing target detection methods in low accuracy.To address this issue,this paper proposes a multi-scale fusion framework(Swin-PAFF)for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure(FPN).Firstly,to tackle the issue of inadequate perceptual image context information in SAR target detection,we propose an end-to-end SAR target detection network with the Transformer structure as the backbone.Furthermore,we enhance the ability of the Swin Transformer to acquire contextual features and cross-information by incorporating a Swin-CC backbone network model that combines the Spatial Depthwise Pooling(SDP)module and the self-attentive mechanism.Finally,we design a cross-layer fusion neck module(PAFF)that better handles multi-scale variations and complex situations(such as sparsity,background interference,etc.).Our devised approach yields a noteworthy AP@0.5:0.95 performance of 91.3%when assessed on the HRSID dataset.The application of our proposed technique has resulted in a noteworthy advancement of 8%in the AP@0.5:0.95 scores on the HRSID dataset.

高盐诱导高血压大鼠心肌间质重构及SHIP-1和IL-6的表达研究

目的:探讨高盐诱导高血压大鼠心肌间质纤维化及SH2结构域的肌醇5-磷酸酶1(SHIP-1)和白介素6(IL-6)的表达。方法:将25只SD雄性大鼠随机分为对照组和模型组,模型组给予高盐饲料(含3%NaCl)喂养12周,对照组给予普通饲料喂养12周。每两周测量大鼠尾动脉收缩压,12周后称取大鼠体重和心脏重量,计算心脏重量指数(心脏重量/体重)。采用苏木精—伊红(HE)染色及Masson染色观察心肌组织病理学改变。分别采用实时荧光定量PCR(RT-qPCR)法和免疫组化法检测SHIP-1和IL-6 mRNA及其蛋白表达。结果:模型组喂养12周后的收缩压和心脏重量指数均高于对照组(P<0.05)。与对照组比较,模型组大鼠心肌组织大量炎性细胞浸润,胶原纤维显著增多,胶原容积分数(CVF)高于对照组(P<0.05)。模型组心肌组织SHIP-1 mRNA及蛋白表达量低于对照组,IL-6 mRNA及蛋白表达量高于对照组(均P<0.05)。结论:高盐诱导高血压大鼠出现心肌间质纤维化重构,同时SHIP-1表达下调,IL-6表达上调。

Development and Control Strategy of Subsea All-Electric Actuators

The development of subsea all-electric Christmas trees is an area of focus in the offshore oil industry worldwide.The main difficulties are associated with the development and control strategies for subsea all-electric actuators,which are the most criti-cal components of subsea Christmas trees.A single-motor-level fuzzy PID control with an integrated working condition detection mod-ule and a three-motor redundant-level deviation strategy with coupled joint synchronous control were proposed in this paper to real-ize the real time determination of algorithm parameters according to the working conditions,solve the rapid redistribution problems,and maintain the fast speed of the servo motor of the subsea all-electric tree valve actuator.A synchronous control electrical system was built,tested,and verified through simulation analysis.Test results show that the two redundant servo motors can still control the all-electric valve actuator and provide good synchronization control capabilities despite the failure of one servo motor,and the verti-cal and horizontal vibration values of the system are within reasonable ranges.The synchronous control strategy can be applied to the synchronous control problem of subsea all-electric production systems,which is of considerable importance for the development of subsea all-electric production systems.