Small Ship Detection Research Articles

An Improved Method for Ship Target Detection Based on YOLOv4

The resolution of remote sensing images has increased with the maturation of satellite technology. Ship detection technology based on remote sensing images makes it possible to monitor a large range and far sea area, which can greatly enrich the monitoring means of maritime departments. In this paper, we conducted research on small target detection and resistance to complex background interference. First, a ship dataset with four types of targets (aircraft carriers, warships, merchant ships and submarines) is constructed, and experiments are conducted on the dataset using the object detection algorithm YOLOv4. The Kmeans++ clustering algorithm is used for a priori frame selection, and the migration learning method is used to enhance the detection effect of the YOLOv4. Second, the model is improved to address the problems of missed detection of small ships and difficulty in resisting background interference: the RFB_s (Receptive Field Block) with dilated convolution is introduced instead of the SPP (Spatial Pyramid Pooling) to enlarge the receptive field and improve the detection of small targets; the attention mechanism CBAM (Convolutional Block Attention Module) is added to adjust the weights of different features to highlight salient features useful for ship detection task, which improve the detection performance of small ships and improve the model’s ability to resist complex background. Compared to YOLOv4, our proposed model achieved a large improvement in mAP (mean Average Precision) from 77.66% to 91.40%.

A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

Small Ship Detection of SAR Images Based on Optimized Feature Pyramid and Sample Augmentation

Small Ship Detection of SAR Images Based on Optimized Feature Pyramid and Sample Augmentation

Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.

Improved YOLOv5s for Small Ship Detection With Optical Remote Sensing Images

Improved YOLOv5s for Small Ship Detection With Optical Remote Sensing Images

YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.

Small Ship Detection Based on Hybrid Anchor Structure and Feature Super-Resolution

Small ships in remote sensing images have blurred details and are difficult to detect. Existing algorithms usually detect small ships based on predefined anchors with different sizes. However, limited by the number of different sizes, it is difficult for anchor-based methods to match small ships of different sizes and structures during training, as they can easily cause misdetections. In this paper, we propose a hybrid anchor structure to generate region proposals for small ships, so as to take full advantage of both anchor-based methods with high localization accuracy and anchor-free methods with fewer misdetections. To unify the output evaluation and obtain the best output, a label reassignment strategy is proposed, which reassigns the sample labels according to the harmonic intersection-over-union (IoU) before and after regression. In addition, an adaptive feature pyramid structure is proposed to enhance the features of important locations on the feature map, so that the features of small ship targets are more prominent and easier to identify. Moreover, feature super-resolution technology is introduced for the region of interest (RoI) features of small ships to generate super-resolution feature representations with a small computational cost, as well as generative adversarial training to improve the realism of super-resolution features. Based on the super-resolution feature, ship proposals are further classified and regressed by using super-resolution features to obtain more accurate detection results. Detailed ablation and comparison experiments demonstrate the effectiveness of the proposed method.

Intelligent deep learning-enabled autonomous small ship detection and classification model

Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation security and profit margin. On autonomous shipping technologies, small ship detection is vital in ensuring shipping safety. With this motivation, this paper presents an efficient optimal mask regional convolutional neural network (Mask-CNN) technique for small ship detection (OMRCNN-SHD) on autonomous shipping technologies. Primarily, the data augmentation process is performed to resolve the issue of the limited number of real-world samples of small ships and helps to detect small ships in most cases accurately. Besides, the Mask RCNN with SqueezeNet model is used to detect ships and the hyperparameter tuning of the SqueezeNet model takes place by the use of the Adagrad optimizer. Furthermore, the Colliding Body's Optimization (CBO) algorithm with the weighted regularized extreme learning machine (WRELM) technique is employed to classify detected ships effectively. The comparative results analysis demonstrates the betterment of the OMRCNN-SHD technique over the current methods with the maximum accuracy of 98.63%.

A Ship Detection Method via Redesigned FCOS in Large-Scale SAR Images

Ship detection in large-scale synthetic aperture radar (SAR) images has achieved breakthroughs as a result of the improvement of SAR imaging technology. However, there still exist some issues due to the scattering interference, sparsity of ships, and dim and small ships. To address these issues, an anchor-free method is proposed for dim and small ship detection in large-scale SAR images. First, fully convolutional one-stage object detection (FCOS) as the baseline is applied to detecting ships pixel by pixel, which can eliminate the effect of anchors and avoid the missing detection of small ships. Then, considering the particularity of SAR ships, the sample definition is redesigned based on the statistical characteristics of ships. Next, the feature extraction is redesigned to improve the feature representation for dim and small ships. Finally, the classification and regression are redesigned by introducing an improved focal loss and regression refinement with complete intersection over union (CIoU) loss. Experimental simulation results show that the proposed R-FCOS method can detect dim and small ships in large-scale SAR images with higher accuracy compared with other methods.

A Generating-Anchor Network for Small Ship Detection in SAR Images

Synthetic aperture radar (SAR) ship detection especially for small ships has issues such as dense distribution of ships, interference from land and small islands. To address these issues, many deep learning (DL) methods, including anchor-based and anchor-free methods, have been successfully migrated from optical scenes to SAR images. However, when the preset scale of anchors does not match well with the ships, it will seriously reduce the detection precision. Due to the lack of anchor-based refinement process, anchor-free methods may generate missing or false alarms in complex scenarios. In this paper, a two-stage ship detection network which can generate anchors is proposed. Firstly, our method generates high-quality anchors by network, which is more beneficial for the network to capture small ships. In addition, the generated anchors are centrally set in the region of ships, which reduces the number of anchors unrelated to ships. Secondly, the receptive field enhancement module is inserted into the feature pyramid network (FPN). It sets different dilation ratios of atrous convolution according to the scale of the feature map, which further enriches the semantic information of the elements in the feature map. Therefore, the network can use the information of a wider region effectively to detect ships. Finally, to verify the effectiveness of our method, extensive experiments are carried out on SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID). The results show that our method has more strong ability of detecting small ships, and achieves better detection performance than some state-of-the-art methods.

Deformable atrous convolution nearshore SAR small ship detection incorporating mixed attention

Deformable atrous convolution nearshore SAR small ship detection incorporating mixed attention

A Study on Small-Scale Ship Detection Based on Attention Mechanism

In ship detection based on optical images, the system typically needs to handle small-scale targets in complex environments owing to special application scenarios, and small-scale targets are easily ignored after a multi-layer convolution is applied in deep learning. A small-ship detection method based on an attention mechanism is therefore proposed in this study. The local attention module acts on the bottom feature prediction layer to highlight the key features and improve the detection ability of small target objects. Meanwhile, a high-level feature prediction layer is combined to classify, detect, and improve the recognition accuracy of the model. In this study, training was conducted on the SeaShips dataset. Because the SeaShips dataset is of a single type and consists of ships of roughly the same size, we changed the size of the default box, which not only improves the detection speed, it also significantly improves the detection accuracy compared with a conventional SSD algorithm.

PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.

Ship detection from scratch in Synthetic Aperture Radar (SAR) images

ABSTRACT Ship detection in Synthetic Aperture Radar (SAR) images has always been a hot topic for research. The development of Deep Neural Networks (DNNs) has strongly promoted the development of computer vision. DNNs are also increasingly applied to SAR ship detection. However, SAR ship detection still faces the following problems: (i) The network used for detection needs to be pre-trained on ImageNet, but there is a large bias between SAR images and ImageNet, which leads to training bias. (ii) The sizes of ship targets vary greatly, and many DNNs do not perform well on multi-scale and small-size SAR ship detection. Therefore, we have designed a SAR ship detector that does not require pre-training. We use DetNet as the backbone network, adopting stacked convolution instead of down-sampling to solve the problem of small object detection and adopt a feature reuse strategy to improve parameter efficiency. In addition, we introduce several branches in the proposal sub-network to provide multi-scale object detection. In the detection sub-network, we use position-sensitive region of interest pooling to improve the prediction accuracy. Experiments on SAR ship dataset prove that our method performs better than some pre-trained networks for small ship detection and complex background ship detection.

ShipRSImageNet: A Large-Scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images

Ship detection in optical remote sensing images has potential applications in national maritime security, fishing, and defense. Many detectors, including computer vision and geoscience-based methods, have been proposed in the past decade. Recently, deep-learning-based algorithms have also achieved great success in the field of ship detection. However, most of the existing detectors face difficulties in complex environments, small ship detection, and fine-grained ship classification. One reason is that existing datasets have shortcomings in terms of the inadequate number of images, few ship categories, image diversity, and insufficient variations. This article publishes a public ship detection dataset, namely ShipRSImageNet, which contributes an accurately labeled dataset in different scenes with variant categories and image sources. The proposed ShipRSImageNet contains over 3435 images with 17 573 ship instances in 50 categories, elaborately annotated with both horizontal and orientated bounding boxes by experts. From our knowledge, up to now, the proposed ShipRSImageNet is the largest remote sensing dataset for ship detection. Moreover, several state-of-the-art detection algorithms are evaluated on our proposed ShipRSImageNet dataset to give a benchmark for deep-learning-based ship detection methods, which is valuable for assessing algorithm improvement. 1

SSS-YOLO: towards more accurate detection for small ships in SAR image

ABSTRACT Aiming at the low detection rate and high false alarm in small ship detection in SAR images, we propose a small-scale ship detection algorithm based on convolutional neural network in this paper. First, we redesign the feature extraction network according to the characters of ship targets in SAR images. The modified network can enrich the spatial and semantics information of small ships. Then, we propose the Path Argumentation Fusion Network (PAFN) to improve the fusion of different feature maps. PAFN uses bottom-up and top-down ways to fuse more location information and semantic information. Both these two optimizations can enhance the detection for small ships. We evaluate our model based on the open SAR-Ship-Dataset and Gaofen-3 SAR images. The experiment results show that our method has excellent performance for small ship detection compared with other deep learning models. Our model improves AP by 6.5% and has higher detection efficiency compared with the baseline YOLOv3 model.

PolSAR Ship Detection Based on Neighborhood Polarimetric Covariance Matrix

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms.

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.

Deep learning for autonomous ship-oriented small ship detection

Small ship detection is an important topic in autonomous ship technology and plays an essential role in shipping safety. Since traditional object detection techniques based on the shipborne radar are not qualified for the task of near and small ship detection, deep learning-based image recognition methods based on video surveillance systems can be naturally utilized on autonomous vessels to effectively detect near and small ships. However, a limited number of real-world samples of small ships may fail to train a learning method that can accurately detect small ships in most cases. To address this, a novel hybrid deep learning method that combines a modified Generative Adversarial Network (GAN) and a Convolutional Neural Network (CNN)-based detection approach is proposed for small ship detection. Specifically, a Gaussian Mixture Wasserstein GAN with Gradient Penalty is utilized to first directly generate sufficient informative artificial samples of small ships based on the zero-sum game between a generator and a discriminator, and then an improved CNN-based real-time detection method is trained on both the original and the generated data for accurate small ship detection. Experimental results show that the proposed deep learning method (a) is competent to generate sufficient informative small ship samples and (b) can obtain significantly improved and robust results of small ship detection. The results also indicate that the proposed method can be effectively applied to ensuring autonomous ship safety.

A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.