Ship Target Detection Research Articles

Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images

ABSTRACT As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects.

Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence

Optical remote sensing ship target detection has become an essential means of ocean supervision, coastal defense, and frontier defense. Accurate, effective, fast, and real-time remote sensing data processing is the critical technology in this field. This paper proposes a real-time detection algorithm for moving targets in low-resolution wide-area remote sensing images, which includes four steps: pre-screening, simplified HOG feature identification, sequence correlation identification, and facilitated Yolo identification. It can effectively detect and track targets in low-resolution sequence data. Firstly, iterative morphological processing was used to improve the contrast of low-resolution ship target profile edge features compared with the sea surface background. Next, the target area after adaptive segmentation was used to eliminate false alarms. As a result, the invalid background information of extensive comprehensive data was quickly eliminated. Then, support vector machine classification of S-HOG feature was carried out for suspected targets, and interference such as islands and reefs, broken clouds, and waves were eliminated according to the shape characteristics of ship targets. The method of multi-frame data association and searching for adjacent target information between frames was adopted to eliminate the interference of static targets and broken clouds with similar contours. Finally, the sequential marks were further trained and learned, and further false alarm elimination was completed based on the clipped Yolo network. Compared with the traditional Yolo Tiny V2/V3 series network, this method had higher computational speed and better detection performance. The F1 number of detection results was increased by 3%, and the calculation time was reduced by 66%.

Statistical Adaptation Loss Improved SMALL Sample Ship Detection Method Based on an Attention Mechanism and Data Enhancement

Synthetic aperture radar (SAR) imagery is a promising data source for ocean activity detection. Ship target detection based on SAR images is widely used in maritime trade and the military. SAR image data are rare, and the amount of public data is small. For applications of SAR image ship target detection, a model with low data dependence, fast iteration and low training cost is needed. In this paper, the balanced positive and negative data enhancement method was used. Through statistical analysis of the training dataset, similar sea areas in the training set are filled with detection targets with comfortable size features. Increasing the proportion of positive samples in the data helps to improve the model detection effect. The regional attention preadaptation mechanism based on statistical analysis was implemented to extract information, and the scale-adaptive loss was combined to improve the detection accuracy of the model. Using the same data, our model exhibited better performance. When using 30% of the data, our model was stable in terms of accuracy and average precision (AP) and maintained detection results similar to the training results achieved using 100% of the dataset.

Infrared ship target segmentation based on Adversarial Domain Adaptation

Infrared ship target segmentation is one of the key technologies for automatically detecting ship targets in ocean monitoring. However, it is a challenging work to achieve accurate target segmentation from the infrared ship image. To improve its segmentation performance, we present an Adversarial Domain Adaptation Network (ADANet) for infrared ship target segmentation, where the labeled visible ship images are used as the source domain and the unlabeled infrared ship images are as the target domain. To address the issue of style difference between the two domains, we preprocess the visible images of the source domain in turn with graying and whitening to convert them into the images with the style of the target domain. For the infrared images in the target domain, we optimize them with a denoising network. Furthermore, to solve the matter of limited receptive field of the discriminator, we design a discriminator based on atrous convolution to improve its discriminative ability. Finally, for the issue of low confidence of the target domain predicted images, we add the information entropy of the target domain predicted images to the adversarial loss. Experimental results on the home-made dataset as well as a public dataset show that infrared ship target segmentation achieves higher mean intersection over union than the state-of-the-art methods without significantly increase of parameters, demonstrating its effectiveness.

SAR image near-shore ship target detection method in complex background

ABSTRACT Due to background clutter in synthetic aperture radar (SAR) images, the detection of dense ship targets suffers from a low detection rate, high false alarm rate, and high missed detection rate. To address this issue, an FSM-DFF-YOLOv5+Confluence algorithm is proposed in this paper for the detection of near-shore ship targets in SAR images with complex backgrounds. First, based on the YOLOv5 target detection algorithm, two improvements are made in the feature extraction network: feature refinement and multi-feature fusion; in the feature extraction network, deformable convolutional neural networks are adopted to change the position of the target sampling points of the convolution to improve the feature extraction capability of the target and the detection rate of ship targets in SAR images with a complex background; in the multi-feature fusion network structure, cascading and parallel pyramids are used in the multi-feature fusion network to realize feature fusion at different levels; the visual perceptual field of feature extraction is expanded by using null convolution to enhance the adaptability of the network to detect near-shore multi-scale ship targets with complex backgrounds and reduce the false alarm rate of ship target detection in SAR images with complex environments. In this way, the DFF-YOLOv5 near-shore ship target detection algorithm is established. Meanwhile, to address the problem of missed detection in near-shore dense ship target detection, this paper adds rectangular convolution kernels to the convolution of the feature extraction network to better realize the feature extraction of dense ship targets in SAR images with complex backgrounds. Besides, the Confluence algorithm instead of non-maximum suppression is used in the prediction stage. Through experiments on the constructed complex background near-shore ship detection dataset, it is indicated that the average accuracy of the FSM-DFF-YOLOv5+Confluence detection algorithm reaches 88.96%, and the recall rate reaches 88.80%.

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%.

Multi-scale ship target detection using SAR images based on improved Yolov5

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery.

In recent years, deep convolutional neural networks (DCNNs) have been widely used in the task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast storage and computational cost of DCNN limits its application to spaceborne or airborne onboard devices with limited resources. In this paper, a set of lightweight detection networks for SAR ship target detection are proposed. To obtain these lightweight networks, this paper designs a network structure optimization algorithm based on the multi-objective firefly algorithm (termed NOFA). In our design, the NOFA algorithm encodes the filters of a well-performing ship target detection network into a list of probabilities, which will determine whether the lightweight network will inherit the corresponding filter structure and parameters. After that, the multi-objective firefly optimization algorithm (MFA) continuously optimizes the probability list and finally outputs a set of lightweight network encodings that can meet the different needs of the trade-off between detection network precision and size. Finally, the network pruning technology transforms the encoding that meets the task requirements into a lightweight ship target detection network. The experiments on SSDD and SDCD datasets prove that the method proposed in this paper can provide more flexible and lighter detection networks than traditional detection networks.

Retracted] FMT Selector: Fourier‐Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

The terminal guidance tracking always has high speed rotating and scale changing. It makes a large margin of error in current tracking algorithms, which can transmit wrong position in the wireless sensor networks of the Internet of Things. In this paper, we propose a selector based on Fourier‐Mellin transformer, which can accurately track the target with high speed rotating. Firstly we use existing detector to get all proposal target per frame. Then, we use Fourier‐Mellin matching to select the best target in the new frame. Our dataset is the ship video which is created by HRSC2016 in rotating. Experimental results show that the accuracy of this method is up to 89.8%. Compared with other traditional tracking algorithms, it has a leap forward. The FMT selector can be applied to the back end of the detector, and it can achieve a feasible effect in the field of terminal guidance tracking.

Ship Detection in SAR Images Based on Multilevel Superpixel Segmentation and Fuzzy Fusion

Superpixel can maintain the boundary of the target and reduce the influence of speckle noise, which has been widely applied to synthetic aperture radar (SAR) image target detection. But the size of the superpixel has a great impact on the performance of superpixel-based SAR target detection algorithms. To solve this problem, we propose a multi-level ship target detection algorithm based on superpixel segmentation. Firstly, the SAR images are segmented in different levels with different superpixel sizes. Different descriptions of the SAR images are obtained in different levels. Secondly, we determine the feature of the superpixels in each level. And in order to enhance the adaptability of the proposed algorithm, we propose an adaptive distance calculation method to select the contrast superpixels in each level. Thirdly, the soft detection results are realized in each level by using the fuzzy C-means (FCM) algorithm. At last, the soft detection results obtained in different levels are fused by a new fusion strategy to achieve the final ship target detection result. The influences caused by different superxiel sizes can be effectively eased by fusion. Experiments in different SAR images have verified the effectiveness of the proposed algorithm in accurately detecting ship targets and insensitivity to the superpixel size.

A Super Lightweight and Efficient SAR Image Ship Detector

The realm of Synthetic Aperture Radar (SAR) ship detection has witnessed widespread adoption of deep learning, owing to its exceptional detection accuracy and end-to-end capabilities. Despite these advantages, the current SAR ship target detection methods still face the challenge of detecting small-scale targets and are difficult to be deployed on satellite platforms due to their complex models and huge computational effort. To overcome these problems, based on the YOLOv5 architecture, we present a super lightweight and efficient SAR ship target detection method named SLit-YOLOv5. Our proposed model comprises two essential components, IMNet and Slim-BiFPN. IMNet serves as the backbone feature extraction network, significantly enhancing the feature extraction capability while reducing the number of parameters by half. Slim-BiFPN achieves adaptive fusion of multi-scale features with fewer parameters. To validate the proposed model, we conducted an experimental evaluation on the SAR ship detection dataset (SSDD), and the results show that our SLit-YOLOv5 model outperforms the currently popular lightweight SAR ship target detection methods with high detection accuracy, low floating-point operations, and very few params.

A Feature-Map-Based Method for Explaining the Performance Degradation of Ship Detection Networks

The unknowability of the inner workings limits the magnitude of performance improvement of ship target detection networks in synthetic aperture radar (SAR) images under Gaussian noise. However, none of the existing interpretation methods explain the phenomenon of network changes under noise. The feature map can visually reflect the changes in image delivery in the network, and some metrics can quantitatively characterize the degree of network performance degradation in a noise environment. So in this paper, we propose a comprehensive analysis method that integrates texture and brightness features of the internal feature map of the network to clarify the change process of target features under Gaussian noise. First, we analyzed the degradation of three target detection networks under different levels of Gaussian noise; then, the feature maps of four convolution layers were sampled and visualized for qualitative analysis; finally, the texture and brightness features were extracted for quantitative characterization of the feature amount changes. We experimentally validated the method on publicly available SSDD radar datasets. The networks were extremely sensitive to Gaussian noise, and the mean Average Precision decreased by up to 96.3%. The angular second moment and entropy texture feature values of the feature map could drop and rise 59.10% and 97.81%, respectively, while the brightness value could increase up to 100.92%. This indicates that noise changes the structure of feature maps and reduces the amount of effective information.

Infrared ship target detection algorithm based on improved YOLOX-S

Infrared ship target detection algorithm based on improved YOLOX-S

Infrared ship target detection algorithm based on YOLOv5

Infrared ship target detection algorithm based on YOLOv5

Review of deep learning-based algorithms for ship target detection from remote sensing images

Review of deep learning-based algorithms for ship target detection from remote sensing images

Performance Evaluation of Ship Target Detection Algorithms Based on Deep Learning

Performance Evaluation of Ship Target Detection Algorithms Based on Deep Learning

Automatic SAR Ship Detection Based on Multifeature Fusion Network in Spatial and Frequency Domains

SAR ship detection is sensitive to the interference of inshore background, disturbance of strong wind and waves. The similar textures of the neighbor objects in SAR images affect the detection performance. As a remarkable indicator, textural information in the frequency domain characterizes the subtle textural differences between an object and its surroundings. Inspired by this, a multi-feature fusion network (MFFN) for SAR ship detection is constructed in this paper, which can obtain contour and detail information of a SAR image for detecting ships from their background. Firstly, spatial and frequency information of ship targets, which characterizes the whole and subtle textural information of ship targets, are extracted by a double-backbone network with Haar wavelet transform. Afterward, a binary domain feature pyramid network (BDFPN) with feature fusion block (FFB) is applied to fuse the spatial, frequency textural information of ship targets to obtain the fused feature maps with a top-down structure. Finally, those feature maps are adopted through the region proposal network for detecting ship targets from original images. The experimental results show that the proposed method achieves greater performance and more accurate detection results in unique situations in the state-of-the-art SAR ship detection data set (SSDD).

A Two-Stage Hierarchical One-Class Classification Structure for HFSWR Ship-Target Detection

A Two-Stage Hierarchical One-Class Classification Structure for HFSWR Ship-Target Detection

An Anchor-Free Detection Algorithm for SAR Ship Targets with Deep Saliency Representation

Target detection in synthetic aperture radar (SAR) images has a wide range of applications in military and civilian fields. However, for engineering applications involving edge deployment, it is difficult to find a suitable balance of accuracy and speed for anchor-based SAR image target detection algorithms. Thus, an anchor-free detection algorithm for SAR ship targets with deep saliency representation, called SRDet, is proposed in this paper to improve SAR ship detection performance against complex backgrounds. First, we design a data enhancement method considering semantic relationships. Second, the state-of-the-art anchor-free target detection framework CenterNet2 is used as a benchmark, and a new feature-enhancing lightweight backbone, called LWBackbone, is designed to reduce the number of model parameters while effectively extracting the salient features of SAR targets. Additionally, a new mixed-domain attention mechanism, called CNAM, is proposed to effectively suppress interference from complex land backgrounds and highlight the target area. Finally, we construct a receptive-field-enhanced detection head module, called RFEHead, to improve the multiscale perception performance of the detection head. Experimental results based on three large-scale SAR target detection datasets, SSDD, HRSID and SAR-ship-dataset, show that our algorithm achieves a better balance between ship target detection accuracy and speed and exhibits excellent generalization performance.

Analysis on the seismic wave caused by low frequency sound source in shallow sea based on multi-transmitting formula artificial boundary

The elastic waves propagating in seabed caused by sailing ships are called ship seismic waves, which can be used to identify ship targets. The wave components and the influences of source frequency, source depth and depth of seawater to the seismic waves are important to the application of seismic wave to detect ship targets. Thus, a forward numerical simulation of seismic waves in shallow sea excited by low frequency sources in time domain was carried out with finite element method based on the Multi-Transmitting Formula artificial boundary. The numerical results show that the body waves and conical waves decay faster in space, but the interface wave decays relatively slow. Multi-transmitting Formula (MTF) artificial boundary has achieved good transmitting effect for longitudinal wave, upstream and downstream acoustic wave and transverse wave. When the source frequency is very low and the seawater layer is shallow, due to the effect of low-frequency cutoff, there is no normal mode wave which can propagate without attenuation in the seawater waveguide, and only the interface wave is propagating near the sea bottom surface. When the frequency of the source or the depth of the seawater layer increases, the effect of low frequency cutoff in the shallow water waveguide is weakened, and the normal mode waves propagating in seawater layer are gradually excited. The intensity of the interface wave caused by low-frequency point sound source mainly depends on the distance between the source and the seafloor.