Ship Target Research Articles

A Torpedo Target Recognition Method Based on the Correlation between Echo Broadening and Apparent Angle

As acoustic decoys can simulate the scale of the target through orderly control of the echo delay, simulated acoustic decoys have scale characteristics similar to those of the scaled target. Consequently, simulated acoustic decoys make it difficult for active acoustic homing torpedoes to recognize acoustic decoys through traditional echo broadening or apparent angle. This will lead to a decrease in the anti-interference capability of torpedoes. In combat, acoustic decoys deceive torpedoes and deviate from the tracking course so that torpedoes cannot find the real target, or waste the range, eventually failing to strike the target and failing in combat. The accurate underwater target scale recognition of active acoustic homing torpedoes is considered a difficult technique. In this paper, we propose a target recognition method based on the correlation between target echo broadening and apparent angle. This specific simulation example shows that conventional target scale recognition methods cannot distinguish between suspended and homing acoustic decoys with virtual scale. By contrast, the target scale recognition method proposed in this paper can accurately distinguish between suspended and homing acoustic decoys with virtual scale at close range, under non-positive transverse port angle conditions. This method improves the anti-interference capability of torpedoes. In addition, it can improve the accuracy of active sonar recognition scale targets of ships, which guide active sonar target recognition.

An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism.

As a computing platform that can deal with problems independently and adapt to different environments, the brain-inspired function is similar to the human brain, which can effectively make use of visual targets and their surrounding background information to make more efficient and accurate decision results. Currently synthetic aperture radar (SAR) ship target detection has an important role in military and civilian fields, but there are still great challenges in SAR ship target detection due to the problems of large span of ship scales and obvious feature differences. Therefore, this paper proposes an improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism, which efficiently focuses on target information ignoring the interference of complex background. First of all, most target detection algorithms are based on the anchor method, which requires a large number of anchors to be defined in advance and has poor generalization capability and performance to be improved in multi-scale ship detection, so this paper adopts an anchor-free detection network to directly enumerate potential target locations to enhance algorithm robustness and improve detection performance. Secondly, in order to improve the SAR ship target feature extraction capability, a dense connection module is proposed for the deep part of the network to promote more adequate deep feature fusion. A visual attention module is proposed for the shallow part of the network to focus on the salient features of the ship target in the local area for the input SAR images and suppress the interference of the surrounding background with similar scattering characteristics. In addition, because the SAR image coherent speckle noise is similar to the edge of the ship target, this paper proposes a novel width height prediction constraint to suppress the noise scattering power effect and improve the SAR ship localization accuracy. Moreover, to prove the effectiveness of this algorithm, experiments are conducted on the SAR ship detection dataset (SSDD) and high resolution SAR images dataset (HRSID). The experimental results show that the proposed algorithm achieves the best detection performance with metrics AP of 68.2% and 62.2% on SSDD and HRSID, respectively.

Feasibility Analysis of the Effects of Scrubber Installation on Ships

A feasibility analysis was conducted on a crude oil tanker. Differences in power generation and fuel consumption of the diesel generators based on the noon report of the specified marine vessel were analyzed for similar periods before and after the scrubber application. Accordingly, the changes in releasing amounts of emissions were calculated. Then, a financial evaluation was realized with three economic indicators. As a result of the analysis performed, it is found that the power generation and fuel consumption realized by each diesel generator are increased after scrubber application. Nevertheless, its implementation on board may be considered acceptable in light of the economic findings. However, it has been determined that scrubber implementation causes an increase in all pollutants’ amounts except SOX. Therefore, it contradicts the zero-emission ship target and decarbonization strategy stated by IMO.

A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea.

ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors.

A Lightweight Model for Ship Detection and Recognition in Complex-Scene SAR Images

SAR ship detection and recognition are important components of the application of SAR data interpretation, allowing for the continuous, reliable, and efficient monitoring of maritime ship targets, in view of the present situation of SAR interpretation applications. On the one hand, because of the lack of high-quality datasets, most existing research on SAR ships is focused on target detection. Additionally, there have been few studies on integrated ship detection and recognition in complex SAR images. On the other hand, the development of deep learning technology promotes research on the SAR image intelligent interpretation algorithm to some extent. However, most existing algorithms only focus on target recognition performance and ignore the model’s size and computational efficiency. Aiming to solve the above problems, a lightweight model for ship detection and recognition in complex-scene SAR images is proposed in this paper. Firstly, in order to comprehensively improve the detection performance and deployment capability, this paper applies the YOLOv5-n lightweight model as the baseline algorithm. Secondly, we redesign and optimize the pyramid pooling structure to effectively enhance the target feature extraction efficiency and improve the algorithm’s operation speed. Meanwhile, to suppress the influence of complex background interference and ships’ distribution, we integrate different attention mechanism into the target feature extraction layer. In addition, to improve the detection and recognition performance of densely parallel ships, we optimize the structure of the model’s prediction layer by adding an angular classification module. Finally, we conducted extensive experiments on the newly released complex-scene SAR image ship detection and recognition dataset, named the SRSDDv1.0 dataset. The experimental results show that the minimum size of the model proposed in this paper is only 1.92 M parameters and 4.52 MB of model memory, which can achieve an excellent F1-Score performance of 61.26 and an FPS performance of 68.02 on the SRSDDv1.0 dataset.

Simulation method for infrared radiation transmission characteristics of typical ship targets based on optical remote sensing

SummaryIn order to meet the needs of the application of optical detection and recognition of ship targets in complex sea environment, based on the full link of optical remote sensing detection, the radiation transmission mechanism of ship target characteristics is analyzed. Then, the radiation characteristics model of ship target, the radiation model of ship target thermal wake, and the radiation characteristics model of sea background are established, and the simulation calculation of focal plane radiation characteristics for sea surface ship target is realized according to the space‐based detection and imaging mechanism; Finally, based on the simulation calculation model, the variation law of ship target radiation characteristics under different sea surface conditions and different imaging time are analyzed, which can provide theoretical basis and scientific guidance for the key index design of Space‐Based Surveillance and detection system and the optimization of target detection and recognition algorithm.

A new feature extraction method of ship radiated noise based on variational mode decomposition, weighted fluctuation-based dispersion entropy and relevance vector machine

Due to the changeable circumstance and the continuous improvement of ship manufacturing technique, ship radiated noise (SRN) has become more and more complex, which makes it more essential to extract its features. Moreover, the target ships can be quickly classified by feature extraction, thus occupying the advantage of maritime confrontation. For the sake of improved feature extraction technology, a new feature extraction method of SRN based on optimized variational mode decomposition by tuna swarm optimization (TSO-VMD), weighted fluctuation-based dispersion entropy (WFDE) and optimized relevance vector machine by sparrow search algorithm (SSA-RVM) is proposed, moreover, the features of seven types of SRN are extracted respectively. To solve the weakness that decomposition level K and penalty factor α must to be preset artificially in VMD, TSO-VMD is proposed. To select optimal width factor and super parameter of RVM, SSA-RVM is proposed. In the first place, decompose SRN into a range of intrinsic modal functions (IMFs) by TSO-VMD, calculate the K-nearest neighbor mutual information (KNN-MI) value between each IMF and the original signal, and normalize it to obtain normalized KNN-MI (nor-KNNMI). Next, select the IMF with the largest nor-KNNMI value as the feature vector, and 60 samples are randomly selected from the feature vector and their fluctuation-based dispersion entropy (FDE) are calculated. Afterwards, select the nor-KNNMI value corresponding to the feature vector to weight the FDE value to obtain the WFDE value, and use the WFDE value to classify SRN. In the end, input the WFDE value of each sample into SSA-RVM for identification. The experimental results show that the identification rate of the proposed method is over 90%. Therefore, the proposed method can accurately and efficiently extract the features of SRN.

Towards real-time detection of ships and wakes with lightweight deep learning model in Gaofen-3 SAR images

Synthetic aperture radar (SAR) is an important tool for achieving accurate and efficient monitoring of maritime traffic by identifying ships and wakes. Ship wake can not only assist in ship detection, but also invert ship navigation information, which has attracted wide attention. However, most of the work on ship wake detection is still through non-deep learning methods. Limited by factors such as unclear features, complex background interference and insufficient label information in large-scale SAR images, deep learning methods have developed slowly in the field of ship wake detection. In this paper, we propose a lightweight deep learning network for ship and wake detection. In the framework, a YOLO structure based deep learning is implemented to achieve ship and wake classification and localization. For this purpose, we initially create a Gaofen-3 SAR dataset containing about 1000 pairs of ships and wakes. To streamline the deep learning network, based on the background and pixel characteristics of SAR images, we design a combined convolutional module in combination with an attention mechanism, which replaces the convolutional layers in the backbone to extract more diverse feature maps at a low cost. In addition, in view of the geometric features of ship wake, the optimized design is conducted in the target prediction stage, and the angle-related loss function is developed to guide the training of the network, which not only ensures the detection of the ship target, but also greatly improves the accuracy of wake line detection. Experimental results show that the developed lightweight network achieves accurate and stable detection of ships and wakes in the dataset, and it has excellent generalization ability in complex background and weak target detection. The performance is comparable to the state-of-the-art models, where the average precision of ship and wake detection reaches 77.86% and 97.29%, respectively. Particularly, the average angle error between the predicted and actual values of wake lines is reduced by 3.476°. Moreover, the low energy consumption (model size is one-sixth of YOLOv4) and real-time detection (>23.1 FPS) of the algorithm offer great potential for embedded deployment.

Ship Target Recognition Based on Context-Enhanced Trajectory

Ship target recognition based on trajectories has great potential in the field of target recognition. In the existing research, the context information is ignored, which limits the improvement of ship target recognition ability. In addition, the process of trajectory feature extraction is complex, and recognition accuracy needs to be further improved. In this paper, a ship target recognition method based on a context-enhanced trajectory is proposed. The maritime context knowledge base is constructed to enhance the trajectory information and to improve the separability of different types of target trajectories. A deep learning model is used to extract trajectory features and context features automatically. Offline training and online recognition are adopted to complete the target recognition task. Experimental analysis and verification are carried out using the automatic identification system (AIS) dataset. The recognition accuracy increases by 7.91% after context enhancement, which shows that the context enhancement is efficient. The proposed method also has a strong anti-noise ability. In the noisy environment set in this paper, the recognition accuracy of the proposed method is still maintained at 86.13%.

Classification for Synthetic Aperture Radar Ship Image of the Ship Targets under the Few-shot Condition

Deep learning algorithms have shown outstanding results for the categorization of synthetic aperture radar (SAR) images. But because there is a conflict between the deep learning techniques' broad parameter space and the scanty labeled samples of ship targets, most deep learning models are unable to provide satisfying outcomes under the few-shot scenario. Classification for SAR ship images often faces the few-shot condition because they are sensitive to system parameters, so that the amount of effective SAR images is very low. To this end, this article proposes a method based on graph neural network (GNN). An end-to-end classification model was trained by this method. Moreover, our method extracts multi-scale features of the targets to make classification effect better. The dropout layers are added into the graph convolution layer to reduce the overfitting in few-shot classification. Finally, our model is applied to the mixed dataset of the simulated and real data to verify effectiveness of our method. The experimental findings presented in this research show that our method outperforms other cutting-edge approaches in terms of categorization rate.

Wind resistance aerial path planning for efficient reconstruction of offshore ship

ABSTRACT When the unmanned aerial vehicle (UAV) is applied to three-dimensional (3D) reconstruction of the offshore ship, it faces two problems: the battery capacity limitation of the UAV and the disturbance of the wind in the environment. Wind disturbance is generally not considered in the path planning process of the existing UAV 3D reconstruction path planning research. Therefore, the planned path is only suitable for no-wind or light-wind scenarios. For the 3D reconstruction of ship targets, we propose a UAV path planning method that can satisfy both reconstruction efficiency and wind disturbance resistance requirements. Firstly, the concept of model surface complexity is proposed to generate a more efficient candidate view set. Secondly, the Min–Max strategy and a new viewpoint construction method are used to generate the initial path. Thirdly, combined with the wind field model, a method for generating a stable path against wind disturbance based on the idea of interval optimization is proposed. Experimental results demonstrate that our method can adaptively determine the number of sample points and viewpoints according to ship’s geometric characteristics and further reduce the number of viewpoints without significantly affecting the reconstruction quality; the path planned by our method is also stable against wind disturbance.

Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism.

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

TISD: A Three Bands Thermal Infrared Dataset for All Day Ship Detection in Spaceborne Imagery

The development of infrared remote sensing technology improves the ability of night target observation, and thermal imaging systems (TIS) play a key role in the military field. Ship detection using thermal infrared (TI) remote sensing images (RSIs) has aroused great interest for fishery supervision, port management, and maritime safety. However, due to the high secrecy level of infrared data, thermal infrared ship datasets are lacking. In this paper, a new three-bands thermal infrared ship dataset (TISD) is proposed to evaluate all-day ship target detection algorithms. All images are from SDGSAT-1 satellite TIS three bands RSIs of the real world. Based on the TISD, we use the state-of-the-art algorithm as a baseline to do the following. (1) Common ship detection methods and existing ship datasets from synthetic aperture radar, visible, and infrared images are elementarily summarized. (2) The proposed standard deviation of single band, correlation coefficient of combined bands, and optimum index factor features of three-bands datasets are analyzed, respectively. Combined with the above theoretical analysis, the influence of the bands’ information input on the detection accuracy of a neural network model is explored. (3) We construct a lightweight network based on Yolov5 to reduce the number of floating-point operations, which is beneficial to reduce the inference time. (4) By utilizing up-sampling and registration pre-processing methods, TI images are fused with glimmer RSIs to verify the detection accuracy at night. In practice, the proposed datasets are expected to promote the research and application of all-day ship detection.

Nighttime infrared ship target detection based on Two-channel image separation combined with saliency mapping of local grayscale dynamic range

The automatic detection of infrared ship targets at night is the key research of intelligent maritime monitoring. Because the infrared maritime small targets are always dim, owning only simple texture, and variable in shape in voyage, deep learning is not so suitable for these kinds of target detection. In this paper, two-channel image separation combined with saliency mapping of local grayscale dynamic range (TCS-SMoLGDR) algorithm for ship target detection is proposed. The saliency mapping of local grayscale dynamic range (SMoLGDR) is proposed by taking full advantage of the feature that the infrared ship target at night owns the largest grayscale dynamic range. A saliency map is generated through SMoLGDR, in which the target area is significantly enhanced and the background is effectively suppressed. On the basis of the saliency map, the connected domain mean strategy is used to determine the real target area, which is beneficial to retain the complete target. For better segmentation of the target with uneven grayscale distribution from the background, this paper proposes a two-channel image separation (TCS) method to separate the local image of the target area into a bright channel image and a dark channel image, so that the grayscale distribution of targets in sub-images becomes relatively uniform. Finally, edge-guided binarization is used to extract the target. The experiment results show fully that the algorithm proposed in this paper can achieve the effective detection of the small dim targets in the nighttime infrared maritime image, and the detection accuracy is better than the comparison algorithms.

Techno-Economic Analysis of NH3 Fuel Supply and Onboard Re-Liquefaction System for an NH3-Fueled Ocean-Going Large Container Ship

This study proposed the integrated design of an NH3 fuel supply system and a re-liquefaction system for an ocean-going NH3-fueled ship. The target ship was a 14,000 TEU large container ship traveling from Asia to Europe. The NH3 fuel supply system was developed to feed the liquid fuel at 40 °C and 80 bar and cope with the re-circulated fuel with the sealing oil. Its power consumptions and SECs ranged from 56.4 to 157.5 kW and from 0.0063 to 0.009 kWh/kg, respectively. An onboard re-liquefaction system with a vapor compression refrigeration cycle was also designed to liquefy the BOG from the fuel tank. The re-liquefaction system’s exergy efficiency and SEC were 34.71% and 0.224 kWh/kg, respectively. The equipment with the most exergy destruction was the heat exchangers, accounting for 60% of the total exergy destruction. NPV analysis found that it is recommended to introduce the re-liquefaction system to the target ship. At the NH3 price of USD 250/ton, the reasonable cost of the re-liquefaction system is less than USD 1 million. According to LCC, NH3 fuel is economically feasible if the carbon tax is more than USD 80/ton and the NH3 price is around USD 250/ton.

A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R3Det

The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship images due to complex scenes and large-target-scale differences, an improved R3Det algorithm is proposed in this paper. On the basis of R3Det, a feature pyramid network (FPN) structure is replaced by a search architecture-based feature pyramid network (NAS FPN) so that the network can adaptively learn and select the feature combination update and enrich the multiscale feature information. After the feature extraction network, a shallow feature is added to the context information enhancement (COT) module to supplement the small target semantic information. An efficient channel attention (ECA) module is added to make the network gather in the target area. The improved algorithm is applied to the ship data in the remote sensing image data set FAIR1M. The effectiveness of the improved model in a complex environment and for small target detection is verified through comparison experiments with R3Det and other models.

Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement.

Due to the multiscale characteristics of ship targets in ORSIs (optical remote sensing images), ship target detection in ORSIs based on depth learning is still facing great challenges. Aiming at the low accuracy of multiscale ship target detection in ORSIs, this paper proposes a ship target detection algorithm based on multiscale feature enhancement based on YOLO v4. Firstly, an improved mixed convolution is introduced into the IRes (inverted residual block) to form an MIRes (mixed inverted residual block). The MIRes are used to replace the Res (residual block) in the deep CSP module of the backbone network to enhance the multiscale feature extraction capability of the backbone network. Secondly, for different scale feature maps' perception fields, feature information, and the scale of the detected objects, the multiscale feature enhancement modules—SFEM (small scale feature enhancement module) and MFEM (middle scale feature enhancement module)—are proposed to enhance the feature information of the middle- and low-level feature maps, respectively, and then the enhanced feature maps are sent to the detection head for detection. Finally, experiments were implemented on the LEVIR-ship dataset and the NWPU VHR-10 dataset. The accuracy of the proposed algorithm in ship target detection reached 79.55% and 90.70%, respectively, which is improved by 3.25% and 3.56% compared with YOLO v4.

A Two-Stage Path Planning Algorithm Based on Rapid-Exploring Random Tree for Ships Navigating in Multi-Obstacle Water Areas Considering COLREGs

A two-stage ship path planning method is proposed, based on the Rapid-exploring Random Tree (RRT) algorithm, which is composed of global path planning and local path planning, addressing the important problem of finding an economical and safe path from start to destination for ships under dynamic environment, especially in waters with multiple obstacles and multiple target ships. The global path planning takes into consideration the ship draft and Under Keel Clearance to find navigable water using RRT, and reduces the path length and waypoints based on elliptic sampling and smoothing. In the local path planning, a dynamic collision risk detection model is constructed by introducing the Quaternion Ship Domain under a dynamic environment, and the restrictions of ship manoeuvrability and COLREGs are also involved. The simulation results show that the proposed model can find a satisfactory path within a few iterations, and keep clear of both static obstacles and dynamic ships. The research can be used to make and verify planned ship routes before sailing and to guide officers to make decisions regarding collision avoidance.

Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction

In the water transportation, ship speed estimation has become a key subject of intelligent shipping research. Traditionally, Automatic Identification System (AIS) is used to extract the ship speed information. However, transportation environment is gradually becoming complex, especially in the busy water, leading to the loss of some AIS data and resulting in a variety of maritime accidents. To make up for this deficiency, this paper proposes a vessel speed extraction framework, based on Unmanned Aerial Vehicle (UAV) airborne video. Firstly, YOLO v4 is employed to detect the ship targets in UAV image precisely. Secondly, a simple online and real time tracking method with a Deep association metric (Deep SORT) is applied to track ship targets with high quality. Finally, the ship motion pixel is computed based on the bounding box information of the ship trajectories, at the same time, the ship speed is estimated according to the mapping relationship between image space and the real space. Exhaustive experiments are conducted on the various scenarios. Results verify that the proposed framework has an excellent performance with average speed measurement accuracy is above 93% in complex waters. This paper also paves a way to further predict ship traffic flow in water transportation.