Ship Target Research Articles

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model’s adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments.

Finite element simulation analysis of vibration transmission characteristics of double shell based on ANSYS

Ship noise contains a large number of low-frequency line spectrum features. It is difficult to distinguish where they come from and even more difficult to identify the ship’s target types using the line spectrum. Therefore, research on ship targets’ vibration transmission characteristics and radiation noise is of great importance for underwater target recognition. From the perspective of underwater acoustic target recognition, this article establishes a simplified finite element model of the double shell using ANSYS, calculates the displacement response of the double shell under harmonic loads, and conducts modal analysis, harmonic response analysis, and transient dynamic analysis, which verifies the consistency of vibration between the inner shell and outer shell. Marine experimental data show that the low-frequency line spectrum of ship noise is related to the vibration frequency of ship main engines. It may be caused by diesel engine vibration transmitted through ship hull vibration and can be used as an auxiliary feature for underwater acoustic target recognition.

A Classification Method for Marine Surface Floating Small Targets and Ship Targets

A Classification Method for Marine Surface Floating Small Targets and Ship Targets

Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision

Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition.

Composite scattering computation from multiple dielectric targets of one-dimensional fractal sea surface using MOM

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as ‘dielectric or conductive object + rough surface’, ‘dielectric or conductive floating object +rough’ and it has certain generalization and reference, and it lays a foundation for solving the composite scattering coefficient of multiple medium targets in two-dimensional fractal sea surface.

Optimal path planning for a ship in coastal waters with deep Q network

With the development of autonomous ships, complete automation of passage planning has become an imminent priority. However, existing A* algorithms have the disadvantage of generating paths close to land because they prioritize minimizing the navigation distance. Therefore, this study proposes a method based on a deep Q network to effectively implement reward-and-penalty strategies considering required navigation areas and non-navigable areas throughout the port-to-port distance. First, the Busan and Gwangyang Ports were selected as the target areas and a container ship was selected as the target ship. Initially, non-navigable and reward areas were designated based on the water depth and electronic navigational chart information. Thereafter, we conducted experiments using algorithms in three types of environments: normal conditions, turbulent weather, and obstacle-involved environments. Furthermore, we employed the Douglas–Peucker algorithm to eliminate excessive waypoints. Experimental results demonstrated that the path planning of a ship obtained using a deep Q network involved more efficient and safer decisions for ship navigation. Furthermore, the navigation distance was reduced by 1.77% compared to the passage plan used by actual ships. The proposed approach is advantageous for automatically deriving the optimal mid-range path of ships and can thus contribute toward improving maritime safety and efficiency.

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification.

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

YOLO-Ships: Lightweight ship object detection based on feature enhancement

Due to the influences of complex shore-based backgrounds, ship scale changes and occlusions, most deep learning-based ship target detection algorithms yield false and missed detections, and the number of required model parameters is large. Therefore, we present a lightweight and effective method called YOLO-Ships. An improved lightweight Ghost module was used to enhance the ability of the network to extract ship features. In addition, we designed a ship feature enhancement module and a lightweight C3REGhost module to strengthen the important feature information and improve the detection ability and localization accuracy of the model. Finally, the model loss function was redesigned to improve the convergence speed and accuracy of the model. Our experimental results showed that the mAP@0.5 increased by 5.2% and the number of parameters decreased by 2.89 M compared to those of YOLOv5s, effectively achieving a balance between model lightweightness and good ship detection accuracy.

An enhanced object detection network for ship target detection in SAR images

An enhanced object detection network for ship target detection in SAR images

Local Path Planning Method for Unmanned Ship Based on Encounter Situation Inference and COLREGS Constraints

Local path planning, as an essential technology to ensure intelligent ships’ safe navigation, has attracted the attention of many scholars worldwide. In most existing studies, the impact of COLREGS has received limited consideration, and there is insufficient exploration of the method in complex waters with multiple interfering ships and static obstacles. Therefore, in this paper, a generation method for a time–space overlapping equivalent static obstacle line for ships in multi-ship encounter scenarios where both dynamic and static obstacles coexist is proposed. By dynamically inferring ships’ encounter situations and considering the requirements of COLREGS, the influence of interfering ships and static obstacles on the navigation of the target ship at different times in the near future is represented as static obstacle lines. These lines are then incorporated into the scene that the target ship encountered at the path planning moment. Subsequently, the existing path planning methods were extensively utilized to obtain the local path. Compared with many common path planning methods in random scenarios, the effectiveness and reliability of the method proposed are verified. It has been demonstrated by experimental results that the proposed method can offer a theoretical basis and technical support for the autonomous navigation of unmanned ships.

Wake-Responsive AUV Guidance Assisted by Passive Sonar Measurements

To make an Autonomous Underwater Vehicle (AUV) chase a moving target ship that generates wake, wake-responsive guidance can be used. This scenario is applicable to making an underwater torpedo pursue a moving target ship until hitting the target. The objective of our research is to make an AUV pursue a target ship assisted by passive sonar sensors as well as wake sensors. To track a maneuvering target without losing the target, the AUV applies both passive sonar sensors and two wake sensors. Two wake sensors are utilized to decide the turn direction of the AUV in zig-zag maneuvers. In practice, sharp maneuvers of the target can cause the AUV to exit the target’s wake abruptly. As the target ship’s wake is not detected by wake sensors and the AUV needs to search for the target ship, the AUV’s passive sonar measures the direction of sound generated from the target ship. Thereafter, the AUV chases the target ship until the target’s wake is detected again. As far as we know, our paper is novel in addressing wake-responsive AUV guidance assisted by passive sonar sensors. The effectiveness of the proposed guidance is verified using computer simulations.

Infrared Maritime Small-Target Detection Based on Fusion Gray Gradient Clutter Suppression

The long-distance ship target turns into a small spot in an infrared image, which has the characteristics of small size, weak intensity, limited texture information, and is easily affected by noise. Moreover, the presence of heavy sea clutter, including sun glints that exhibit local contrast similar to small targets, negatively impacts the performance of small-target detection methods. To address these challenges, we propose an effective detection scheme called fusion gray gradient clutter suppression (FGGCS), which leverages the disparities in grayscale and gradient between the target and its surrounding background. Firstly, we designed a harmonic contrast map (HCM) by using the two-dimensional difference of Gaussian (2D-DoG) filter and eigenvalue harmonic mean of the structure tensor to highlight high-contrast regions of interest. Secondly, a local gradient difference measure (LGDM) is designed to distinguish isotropic small targets from background edges with local gradients in a specific direction. Subsequently, by integrating the HCM and LGDM, we designed a fusion gray gradient clutter suppression map (FGGCSM) to effectively enhance the target and suppress clutter from the sea background. Finally, an adaptive constant false alarm threshold is adopted to extract the targets. Extensive experiments on five real infrared maritime image sequences full of sea glints, including a small target and sea–sky background, show that FGGCS effectively increases the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF) by more than 22% and 82%, respectively. Furthermore, its receiver operating characteristic (ROC) curve has an obviously more rapid convergence rate than those of other typical detection algorithms and improves the accuracy of small-target detection in complex maritime backgrounds.

High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n

With the development of the intelligent vision industry, ship detection and identification technology has gradually become a research hotspot in the field of marine insurance and port logistics. However, due to the interference of rain, haze, waves, light, and other bad weather, the robustness and effectiveness of existing detection algorithms remain a continuous challenge. For this reason, an improved YOLOv8n algorithm is proposed for the detection of ship targets under unforeseen environmental conditions. In the proposed method, the efficient multi-scale attention module (C2f_EMAM) is introduced to integrate the context information of different scales so that the convolutional neural network can generate better pixel-level attention to high-level feature maps. In addition, a fully-concatenate bi-directional feature pyramid network (Concatenate_FBiFPN) is adopted to replace the simple superposition/addition of feature map, which can better solve the problem of feature propagation and information flow in target detection. An improved spatial pyramid pooling fast structure (SPPF2+1) is also designed to emphasize low-level pooling features and reduce the pooling depth to accommodate the information characteristics of the ship. A comparison experiment was conducted between other mainstream methods and our proposed algorithm. Results showed that our proposed algorithm outperformed other models by achieving 99.4% of accuracy, 98.2% of precision, 98.5% of recall, 99.1% of mAP@.5, and 85.4% of mAP@.5:.95 on the SeaShips dataset.

A Lightweight Convolutional Neural Network for Ship Target Detection in SAR Images

A Lightweight Convolutional Neural Network for Ship Target Detection in SAR Images

面向对海监视的舰船目标跟踪与航迹融合数据集

Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, uniform data formats, and incomplete data descriptions. We used simulation software to emulate multi-sensor multi-target detection data in complex scenarios, so as to present a dataset tailored for typical maritime surveillance scenarios—targeting ships using 2D radar and Electronic Support Measures (ESM) sensors. The simulation software comprises a scenario generator and a sensor simulator, creating a mature target tracking scenario simulation environment with realistic detection data modeling capabilities. The dataset includes data from 2D radar and ESM sensors, covering typical maritime ship categories, supporting configurations with radiation sources. It is designed for a variety of scenarios such as high-speed motion, dense traffic, multi-sensor data fusion, specific ship detection, and cross-positioning. The dataset contains a total of 368,155 target tracks from 101 ships, spanning a duration of 15,000 seconds. The data format conforms to actual equipment reporting scenarios, while the detection error model accurately reflects real-world conditions. Accuracy assessment and data completeness are ensured through various methods including normality testing of data error, scenario testing of detection rate and false alarm rate, as well as field research. This dataset can provide fundamental data for algorithmic research and validation in the ship's target tracking, trajectory fusion and other related areas.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

YOLO-RSA: A Multiscale Ship Detection Algorithm Based on Optical Remote Sensing Image

Currently, deep learning is extensively utilized for ship target detection; however, achieving accurate and real-time detection of multi-scale targets remains a significant challenge. Considering the diverse scenes, varied scales, and complex backgrounds of ships in optical remote sensing images, we introduce a network model named YOLO-RSA. The model consists of a backbone feature extraction network, a multi-scale feature pyramid, and a rotated detection head. We conduct thorough tests on the HRSC2016 and DOTA datasets to validate the proposed algorithm. Through ablation experiments, we assess the impact of each improvement component on the model. In comparative experiments, the proposed model surpasses other models in terms of Recall, Precision, and MAP on the HRSC2016 dataset. Finally, in generalization experiments, our proposed ship detection model exhibits excellent detection performance across various scenarios. The method can accurately detect multi-scale ships in the image and provide a basis for marine ship monitoring and port management.

Enhancement of Small Ship Detection Using Polarimetric Combination from Sentinel−1 Imagery

Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships.