Ship Detection Approach Research Articles

Efficient Ship Detection System for Maritime Surveillance using Deep Learning Approach

The usage of drones in maritime surveillance is a very effective means to observe ships. As the ship detection is vital for applications such as port monitoring, cross-border surveillance, Moreover, it is not only vital for maritime surveillance but also environmental conservation. Despite its effectiveness, the drone-captured images present itself with its own challenges, specifically when leveraging on a custom dataset which is tailored for specific application. To address the issues, we propose a novel ship detection approach called YOLOv9 with Adan optimizer (YOLOv9-Adan) which is more accurate to increase the efficiency of maritime surveillance. The YOLOv9-Adan model integrates the robust object detection capabilities of the adaptive learning capabilities of the Adan (ADAptive Nesterov momentum algorithm) optimiser, trained on the drone image dataset comprising 3200 images of maritime scenes and ship types in drone view. Our model is trained on a drone-image dataset comprising 3200 images of maritime scenes and ship types in drone views collected from various sources. The experimental results show that our approach using the YOLOv9-Adan model achieves 65.5% mAP, which exceeds the mAP of YOLOv9 by 4.3%. Additionally, This article also provides a comparative analysis of our model YOLOv9-Adan with other existing models in literature with consistently surpassing existing approaches

A two-stage approach for ship detection in restricted visibility based on dehazing and SE-YOLO algorithms

ABSTRACT The rapid and automatic detection of ships in restricted visibility is crucial for protecting the safety of maritime navigation. The frequent presence of fog in maritime environments causes restricted visibility, contributing to a higher occurrence of maritime accidents. Aiming to address ship target detection's low accuracy in restricted visibility weather, this paper proposes an improved dehazing model based on a two-stage ship detection algorithm. First, during the dehazing stage, a specialized model for maritime environments addresses the problem of unclear ship features in foggy images. The model is trained by a combination of synthetic images training and real images fine-tuning. Then, during the detection stage, a modified YOLO network for small ship detection called SE-YOLO is introduced to solve the issue of small ship targets' low detection accuracy. On the one hand, the modified Spatial Pyramid Pooling – Fast(SPPF) module is designed to reduce information loss during feature extraction; on the other hand, the attention mechanisms are integrated to enhance the network's sensitivity to the details of small ship targets and improve the model's overall detection performance for ships. Moreover, to simulate the real sea scene and test the effectiveness of our method, a maritime-haze dataset containing different concentrations of fog and various brightness is made for this research. Finally, The experimental results indicate that, compared to the traditional YOLOv5 method, our method performs better on detecting ships in restricted visibility environments, with the mean average precision (mAP) increased from 62.64% to 77.23%.

A Ship Detection Method in Infrared Remote Sensing Images Based on Image Generation and Causal Inference

Aiming at the problems of scarce public infrared ship data and the difficulty of obtaining them, a ship image generation method based on improved StyleGAN2 is proposed. The mapping network in StyleGAN2 is replaced with a Variational Auto-Encoder, enabling the generated latent variables to retain original image information while reducing computational complexity. This benefits the construction of the image. Additionally, a self-attention mechanism is introduced to capture dependency information between distant features, generating more detailed object representation. By reducing the number of input noises in the generator, the quality of the generated images is effectively enhanced. Experimental results show that the images generated by the proposed method closely resemble the structure, content and data distribution of the original real images, achieving a higher level of detail. Regarding ship detection methods based on deep learning, they often suffer from complex detection networks, numerous parameters, poor interpretability, and limited real-time performance. To address these issues, a lightweight multi-class ship detection method for infrared remote sensing images is designed. This method aims to improve real-time performance while maintaining accurate ship detection. Based on ship detection, an interpretable ship detection approach based on causal reasoning is presented. By integrating singular value decomposition with the Transformer architecture, the model focuses on causal ship features associated with labels in the images. This enhances the model’s robustness against non-causal information, such as background details, and improves its interpretability.

A Deep Learning Approach for Ship Detection Using Satellite Imagery

INTRODUCTION: This paper addresses ship detection in satellite imagery through a deep learning approach, vital for maritime applications. Traditional methods face challenges with large datasets, motivating the adoption of deep learning techniques.
 OBJECTIVES: The primary objective is to present an algorithmic methodology for U-Net model training, focusing on achieving accuracy, efficiency, and robust ship detection. Overcoming manual limitations and enhancing real-time monitoring capabilities are key objectives.
 METHOD: The methodology involves dataset collection from Copernicus Open Hub, employing run-length encoding for efficient preprocessing, and utilizing a U-Net model trained on Sentinel-2 images. Data manipulation includes run-length encoding, masking, and balanced dataset preprocessing.
 RESULT: Results demonstrate the proposed deep learning model's effectiveness in handling diverse datasets, ensuring accuracy through U-Net architecture, and addressing imbalances. The algorithmic process showcases proficiency in ship detection.
 CONCLUSION: In conclusion, this paper contributes a comprehensive methodology for ship detection, significantly advancing accuracy, efficiency, and robustness in maritime applications. The U-Net-based model successfully automates ship detection, promising real-time monitoring enhancements and improved maritime security.

Improved PSO Algorithm-Based Convolutional Neural Network Approach for Ship Detection and Classifications

Improved PSO Algorithm-Based Convolutional Neural Network Approach for Ship Detection and Classifications

Speckle-Free SAR Image Ship Detection.

Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.

H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network

Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.

Phase spectrum based automatic ship detection in synthetic aperture radar images

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar (SAR) Images using phase spectrum. The proposed method mainly contains two stages: Firstly, sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition, which are easily detected only in sea regions. In order to eliminate the influence of land regions in SAR images, a novel land removing method is explored. The removing method employs a Harris corner detector to obtain some image patches belonging to land, and the probability density function (PDF) of land area can be estimated by these patches. Thus, an appropriate land segmentation threshold is accordingly obtained. Secondly, an automatic ship detector based on phase spectrum is proposed. The proposed detector is free from various idealized assumptions and can accurately detect ships in SAR images. Experimental results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weather and all-time working capacity. Especially, in the marine field, SAR can provide numerous high-quality services for fishery management, traffic control, sea-ice monitoring, marine environmental protection, etc. Among them, ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation. Nowadays, most researches are focusing on improving the ship detection accuracy, while the detection speed is frequently neglected, regardless of traditional feature extraction methods or modern deep learning (DL) methods. However, the high-speed SAR ship detection is of great practical value, because it can provide real-time maritime disaster rescue and emergency military planning. Therefore, in order to address this problem, we proposed a novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN). In this approach, we integrated multi-scale detection mechanism, concatenation mechanism and anchor box mechanism to establish a brand-new light-weight network architecture for the high-speed SAR ship detection. We used DS-CNN, which consists of a depthwise convolution (D-Conv2D) and a pointwise convolution (P-Conv2D), to substitute for the conventional convolution neural network (C-CNN). In this way, the number of network parameters gets obviously decreased, and the ship detection speed gets dramatically improved. We experimented on an open SAR ship detection dataset (SSDD) to validate the correctness and feasibility of the proposed method. To verify the strong migration capacity of our method, we also carried out actual ship detection on a wide-region large-size Sentinel-1 SAR image. Ultimately, under the same hardware platform with NVIDIA RTX2080Ti GPU, the experimental results indicated that the ship detection speed of our proposed method is faster than other methods, meanwhile the detection accuracy is only lightly sacrificed compared with the state-of-art object detectors. Our method has great application value in real-time maritime disaster rescue and emergency military planning.

Ship Detection in Polarimetric SAR Images via Variational Bayesian Inference

In this paper, we propose a novel ship detection approach in polarimetric synthetic aperture radar (SAR) images via variational Bayesian inference. First, we express the polarimetric SAR image as a tensor, and decompose the SAR image as the sum of a sparse component associated with ships and a sea clutter component. These components are denoted by some latent variables. Then, we introduce hierarchical priors of the latent variables to establish the probabilistic model of ship detection. By using variational Bayesian inference, we estimate the posterior distributions of the latent variables. Finally, the ship detection result is obtained in the iterative Bayesian inference process. By virtue of the tensor representation of polarimetric SAR image, the proposed approach explicitly uses all the polarization channels of the SAR image, and avoids the possible information loss in scalar polarimetric feature representation. Moreover, the proposed approach needs no sliding windows. The variational Bayesian inference process actually uses all the pixels instead of the limited pixels in sliding windows. Thus, the proposed approach has good ship detection performance and shape preserving ability, which is especially suitable for congested sea areas. Experimental results accomplished over C-band RADARSAT-2 polarimetric SAR images demonstrate that the proposed approach can achieve state-of-the-art ship detection performance.

Ship Detection for Complex Background SAR Images Based on a Multiscale Variance Weighted Image Entropy Method

Ship detection from complex background synthetic aperture radar (SAR) images is a challenging task. Due to varying local clutter and low signal-to-clutter ratio, the most conventional methods fail to yield satisfactory results. An effective ship detection approach for complex background is developed in this letter. The approach measures the local dissimilarity between target and its neighborhood by using the variance weighted information entropy (VWIE) method. In order to enhance targets, an optimal window selection mechanism based on the multiscale local contrast measure is used in the local VWIE calculation. Experiments indicate that the proposed approach can effectively detect ship targets from the complex background SAR images by using the optimal selection mechanism.

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

Ship detection and classification is critical for national maritime security and national defense. Although some SAR (Synthetic Aperture Radar) image-based ship detection approaches have been proposed and used, they are not able to satisfy the requirement of real-world applications as the number of SAR sensors is limited, the resolution is low, and the revisit cycle is long. As massive optical remote sensing images of high resolution are available, ship detection and classification on theses images is becoming a promising technique, and has attracted great attention on applications including maritime security and traffic control. Some digital image processing methods have been proposed to detect ships in optical remote sensing images, but most of them face difficulty in terms of accuracy, performance and complexity. Recently, an autoencoder-based deep neural network with extreme learning machine was proposed, but it cannot meet the requirement of real-world applications as it only works with simple and small-scaled data sets. Therefore, in this paper, we propose a novel ship detection and classification approach which utilizes deep convolutional neural network (CNN) as the ship classifier. The performance of our proposed ship detection and classification approach was evaluated on a set of images downloaded from Google Earth at the resolution 0.5m. 99% detection accuracy and 95% classification accuracy were achieved. In model training, 75× speedup is achieved on 1 Nvidia Titanx GPU.

Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision

The robust detection of ships is one of the key techniques in coastal and marine applications of synthetic aperture radar (SAR). Conventional SAR ship detectors involved multiple parameters, which need to be estimated or determined very carefully. In this paper, we propose a new ship detection approach based on multi-scale heterogeneities under the a contrario decision framework, with a few parameters that can be easily determined. First, multi-scale heterogeneity features are extracted and fused to build a heterogeneity map, in which ships are well highlighted from backgrounds. Second, a set of reference objects are automatically selected by analyzing the saliency of local regions in the heterogeneity map and then are used to construct a null hypothesis model for the final decision. Finally, the detection results are obtained by using an a contrario decision. Experimental results on real SAR images demonstrate that the proposed method not only works more stably for ships with different sizes, but also has better performance than conventional ship detectors.

Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine

Ship detection on spaceborne images has attracted great interest in the applications of maritime security and traffic control. Optical images stand out from other remote sensing images in object detection due to their higher resolution and more visualized contents. However, most of the popular techniques for ship detection from optical spaceborne images have two shortcomings: 1) Compared with infrared and synthetic aperture radar images, their results are affected by weather conditions, like clouds and ocean waves, and 2) the higher resolution results in larger data volume, which makes processing more difficult. Most of the previous works mainly focus on solving the first problem by improving segmentation or classification with complicated algorithms. These methods face difficulty in efficiently balancing performance and complexity. In this paper, we propose a ship detection approach to solving the aforementioned two issues using wavelet coefficients extracted from JPEG2000 compressed domain combined with deep neural network (DNN) and extreme learning machine (ELM). Compressed domain is adopted for fast ship candidate extraction, DNN is exploited for high-level feature representation and classification, and ELM is used for efficient feature pooling and decision making. Extensive experiments demonstrate that, in comparison with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy.

A COARSE-TO-FINE APPROACH FOR SHIP DETECTION IN SAR IMAGE BASED ON CFAR ALGORITHM

Among ship detection methods for SAR image, constant false alarm rate (CFAR) is the most important one. However, several factors, such as detector parameter and distribution of ocean clutter, afiect the performance of CFAR detection. This paper proposes a novel hierarchical complete and operational ship detection approach based on detector parameter estimation and clutter pixel replacement, which is considered a sequential coarse-to-flne elimination process of false alarms. First, a simple barycentric algorithm is adopted to estimate target-window size, and the morphology method is used to estimate false alarm rate for CFAR detector. Second, a clutter pixel replacement approach based on the statistical features of sea clutter is presented to obtain statistically independent, stationary, and Weibull distributed random data for CFAR detector to remove all false alarms. Experimental results of the detection methods on a SAR image dataset show that the proposed approach is efiective in reducing false alarms and obtains a satisfactory ship detection performance.

Context modeling combined with motion analysis for moving ship detection in port surveillance

In port surveillance, video-based monitoring is a valuable supplement to a radar system by helping to detect smaller ships in the shadow of a larger ship and with the possibility to detect nonmetal ships. Therefore, automatic video-based ship detection is an important research area for security control in port regions. An approach that automatically detects moving ships in port surveillance videos with robustness for occlusions is presented. In our approach, important elements from the visual, spatial, and temporal features of the scene are used to create a model of the contextual information and perform a motion saliency analysis. We model the context of the scene by first segmenting the video frame and contextually labeling the segments, such as water, vegetation, etc. Then, based on the assumption that each object has its own motion, labeled segments are merged into individual semantic regions even when occlusions occur. The context is finally modeled to help locating the candidate ships by exploring semantic relations between ships and context, spatial adjacency and size constraints of different regions. Additionally, we assume that the ship moves with a significant speed compared to its surroundings. As a result, ships are detected by checking motion saliency for candidate ships according to the predefined criteria. We compare this approach with the conventional technique for object classification based on support vector machine. Experiments are carried out with real-life surveillance videos, where the obtained results outperform two recent algorithms and show the accuracy and robustness of the proposed ship detection approach. The inherent simplicity of our algorithmic subsystems enables real-time operation of our proposal in embedded video surveillance, such as port surveillance systems based on moving, nonstatic cameras.

GOPCE-Based Approach to Ship Detection

A new approach is proposed for ship detection, based on the generalized optimization of polarimetric contrast enhancement (OPCE). The new approach consists of two steps. For the first step, the OPCE problem will be solved for obtaining optimal polarizations. In this way, the received powers of a sea area are suppressed. For the second step, we construct a function which not only contains the power term but also contains a term associated with the scattering mechanism. Three optimal coefficients will be obtained such that the variance of the function values of a sea clutter area is minimal. In this way, ships on the sea area are detected. Using two examples, we demonstrate the effectiveness of the proposed approach for ship detection.

An AIAC-based Inshore Ship Target Detection Approach

In high spatial resolution sensing image,it was hard to discriminate inshore ships from harbors because of the similarity of gray and texture for both targets. The half ellipse shape of the inshore ship in the contour was one of the features with better distinguish ability.An alterable included angle chain(AIAC)-based curve description was introduced to describe the inshore ship contours in this paper,which was invariant to rotation,scaling and translation.In the following an improved AIAC generation method was presented.Finally an AIAC-based inshore ship detection approach was proposed by describing and matching the counter feature of inshore ship by AIAC.Experiments based on the satellite sensing image demonstrated that the algorithm proposed could solve the inshore ship detection problem in a certain extent.

A Novel Algorithm for Ship Detection in SAR Imagery Based on the Wavelet Transform

Carrying out an effective control of fishing activities is essential to guarantee a sustainable exploitation of sea resources. Nevertheless, as the regulated areas are extended, they are difficult and time consuming to monitor by means of traditional reconnaissance methods such as planes and patrol vessels. On the contrary, satellite-based synthetic aperture radar (SAR) provides a powerful surveillance capability allowing the observation of broad expanses, independently from weather effects and from the day and night cycle. Unfortunately, the automatic interpretation of SAR images is often complicated, even though undetected targets are sometimes visible by eye. Attending to these particular circumstances, a novel approach for ship detection is proposed based on the analysis of SAR images by means of the discrete wavelet transform. The exposed method takes advantage of the difference of statistical behavior among the ships and the surrounding sea, interpreting the information through the wavelet coefficients in order to provide a more reliable detection. The analysis of the detection performance over both simulated and real images confirms the robustness of the proposed algorithm.