A Low Coupling and Lightweight Algorithm for Ship Detection in Optical Remote Sensing Images

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.

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S2D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S2D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S2D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR.

Fine-grained remote sensing ship detection is crucial in a variety of fields, such as ship safety, marine environmental protection, and maritime traffic management. Despite recent progress, current research suffers from the following three major challenges: insufficient features representation, conflicts in shared features, and inappropriate anchor labeling strategy, which significantly impede accurate fine-grained ship detection. To address these issues, we propose FineShipNet as a solution. Specifically, we first propose a novel blend synchronization module, which aims to facilitate the coutilization of semantic information from top-level and bottom-level features and minimize information redundancy. Subsequently, the blend feature maps are fed into a novel polarized feature focusing module, which decouples the features used in classification and regression to create task-specific discriminating features maps. Meanwhile, we adopt the adaptive harmony anchor labeling and propose a novel metric, harmony score, to choose high-quality anchors that can effectively capture the discriminating features of the target. Extensive experiments on four fine-grained remote sensing ship datasets (HRSC2016, DOSR, FGSD2021, and ShipRSImageNet) demonstrate that our FineShipNet outperforms current state-of-the-art object detection methods, achieving superior performance with mean average precision scores of 81.3%, 68.5%, 85.7%, and 63.9%, respectively.

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

High-resolution synthetic aperture radar (SAR) data have been widely used in marine environmental protection, marine environmental monitoring, and marine traffic management. Ship detection is one of the important parts of SAR data for marine applications. This letter focuses on the feature analysis of ships in high-resolution SAR images and proposes an improved optimizing algorithm for ship detection. A fast block detector is designed to extract sea clutter in a uniform local area, and then a constant false alarm rate detector is employed. Based on the kernel density estimation of ships, aspect ratio, and pixel points, ships are identified. TerraSAR-X and COSMO-SkyMed images are used to test our algorithm. The experimental results show that this algorithm can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.

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.

Ship detection plays an important role in the port monitor and management. The difficulty in ship detection is that with the change of ship traffic flow, limited with small ship imaging scales and face the foggy navigation. The traditional ship image detection method has insufficient detection performance, which cannot meet the needs of the port. In order to improve the performance of ship detection in the port, we detect ship by yolov3 detection algorithm to solve the above problems. This ship detection framework based on convolutional neural networks. The characteristics of the ship are extracted layer by layer through the DarkNet53 network, and multi-scale image pyramid features are formed to detect ships of different scales. Specifically, large ships, medium ships, small ships. And uses yolov3 algorithm to achieve ship detection. We have selected four typical port navigation scenes: (1) small traffic flow (2)foggy ship navigation (3)large traffic flow (4) small imaging scale. Experiments show that the ship detection based on yolov3 has high accuracy in the face of complex sea navigation conditions and can cope with the detection requirements of different scenes at sea.

Aggregating transformers and CNNs for salient object detection in optical remote sensing images

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

Ship detection based on remote sensing image can provide effective assistance for maritime reconnaissance, port and fisheries management. This paper mainly presents an improved Faster R-CNN algorithm for ship detection with dilated convolution. Firstly, the image of remote sensing ship is preprocessed. This paper uses dark channel prior method to dehaze image and enhance dehazed image quality by grey world algorithm. Second, The Faster R-CNN is improved in ship detection. The dilated convolutional is introduced into Faster R-CNN to increases its feature extraction capability. Finally, ship detection software platform is built. The remote ship image preprocessing and ship detection network is integrated into an application software. The ship detection network can be trained end to end. Experimental results on the HRSC2016 datasets illustrated that the proposed presents a good performance for ship detection task.

Carrying out an effective monitoring and control of fishing activities is indispensable to guarantee a sustainable exploitation of sea resources. Nowadays, it is widely assumed that spaceborne synthetic aperture radar (SAR) constitutes a valuable and effective tool for this purpose. Nevertheless, automatic interpretation of SAR images is often puzzling. Hence this paper proposes a new framework for the understanding and handling of SAR data based on the wavelet transform. Specifically, a novel algorithm for ship detection is proposed, justified and tested. On the other hand, this paper aims at bringing some insight at ship detection capabilities of light polarimetric systems.

A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

The main aim of optical remote sensing image (ORSI) object detection is to ascertain the area as well as class of all targets in given images. Nowadays, object detection methods established from deep learning are progressively applied to the analysis of the information from ORSI. But because of the compound background as well as extensive object scale shown in ORSI, the difficulty of object detection has increased. There are still some challenges to be solved. First, when the two bounding boxes don't overlap, current indexes like intersection over union (IoU) cannot identify the distance between them. Secondly, the existing methods using convolutional neural network (CNN) could not take advantage of the characteristics of multi-level features so that these methods are not very good at recognizing small-sized objects. To deal with these above problems, this paper comes up with a ORSI object detection method established on multi-level feature fusion as well as improved boundary box regression compared to IoU. Firstly, a new measure called generalized intersection over union (GIoU) is practiced. That metric could measure the similarity betwixt two bounding boxes, regardless of whether they overlap or not. At the same time, we also directly use GIoU as loss. Lastly, a multi-level feature fusion structure is practiced and it is also integrated into the existing convolutional neural network. In this way, our method could take advantage of the multi-level features. Quantitative comparison of the addressed method with the baseline approach on large-scale dataset named Dior are conducted. The comparison with the most advanced method shows that our method has reached the most advanced performance.

A novel method is developed for ship detection in Synthetic Aperture Radar (SRA) images, which is based on image distance computation techniques. Using a second-order hidden Markov mesh model to learn statistical models of images, one can obtain the distance of two images for the purpose of detecting ships. First, the features of an image can be extracted using a method that best matches its statistical model, which is related to dynamic programming. Second, given the state transition matrix and observation distributions within states, statistical distance between images based on the similarity of their statistical models can be estimated. Experimental results demonstrate that this ship detection algorithm can effectively enhance ship target as well as suppress speckle and has better detection precision and lower calculation complexity.

The characteristics of ocean background and target in the high resolution synthetic aperture radar (SAR) images are analyzed. Aiming at the requirements of ship detection in high-resolution synthetic aperture radar image, the accuracy, the intelligent level, a better real-time operation and processing efficiency, we put forward a ship detection algorithm in high resolution SAR images based on support vector machine (SVM). The algorithm designs a pre-training support vector machine classifier to complete the screening of ship target blocks, then the algorithm of optimal entropy thresholds proposed by Kapur, Sahoo, Wong (KSW) will be used on the target area selected for fine detection of ship targets. In this paper, several commercial satellite data, such as TerraSAR-X, Radarsat-2, are used to verify the experiment. Comparing with the classical CFAR detection algorithm, Experimental results show that the algorithm can obtain preferable false alarm rejection effect, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 20% to 35%.