Ship object detection faces the challenge of increasing the difficulty of positioning in hazy environments. Additionally, the latest convolutional neural network (CNN) cannot obtain satisfactory detection results. Therefore, we propose ERDNet, a dual-branch-driven end-to-end network, to improve ship detection accuracy during hazy weather. Specifically, we design a two-branch feature extraction network through complementary attentional fusion to enhance the object feature information of low-quality images. Second, we designed a feature pyramid fusion structure called ERPSA-PAN to aggregate context information effectively. ERPSA-PAN improves the feature fusion capability of the model by suppressing background interference and enhancing useful information. In addition, the spatial-frequency fusion block (SFFB) module with expanded receptive fields is added to the ERDNet detection head to improve the detection ability for multiscale targets. More importantly, we design a robust haze loss to handle different degrees of haze. We introduce two new haze ship datasets, Hazy-SeaShips and Hazy-Boats, which include 17,000 synthetic haze images and 2898 real haze images, respectively, to address the lack of hazy ship image datasets. The images cover variations such as haze thicknesses, ship types, and scales, along with complex backgrounds, and occlusions. The experimental results show that the proposed method is superior to other state-of-the-art (SOTA) methods and achieves relatively competitive results. The source codes, and datasets are available on https://github.com/ZikHH/ERDNet.

Due to the uniqueness and stability of radio frequency fingerprints (RFF), radio frequency fingerprint identification (RFFI) is an important physical layer authentication method in the security of Internet of Things (IoT). However, existing deep-learning-based RFFI methods require a large number of labeled samples to achieve ideal performance. Besides, when the signal length changes, the network structure needs to be redesigned and the entire training process needs to be reconducted. To address this issue, we propose a few shot RFFI method on the basis of self-distillation with no labels (SDINO). Within it, a network termed DPformer is designed, which can adapt signals of varying lengths and is more lightweight. When the signal length changes, there is no need to retrain the network, and it is more lightweight. Simulation results show that, compared with existing methods, the proposed method achieves better recognition performance and more lightweight on LoRa dataset with 30 classes.

Deep learning-based Synthetic Aperture Radar (SAR) ship image processing is essential in both civilian and military applications. However, training deep learning models requires a large dataset of high-resolution SAR ship images. Since SAR sensors are not readily available, obtaining an adequate number of such images remains a significant challenge. The SAR image generation technique is a useful way to create SAR ship images. However, many current methods depend only on location maps as input and do not incorporate guidance for the content of the SAR images, resulting in the production of low-quality images. To tackle these issues, we propose a novel content guided high resolution SAR ship image generation method. This approach incorporates SAR image-specific information to enhance the details of the generated images. We achieve this by separately fusing the input source image with both the source and target location maps, using them as guidance to steer the generation process. Additionally, to effectively transfer this guidance to the target SAR image, we introduce a Feature Transferring Module, which performs affine transformations based on scaling and shifting values learned by a Parameter Learning Module. Moreover, we design a Mask Transformer Module to learn the correlation weight and establish a fine-grained mapping between the source and target SAR images. To train and evaluate the proposed model, we have re-labeled the land and sea regions in the HRSID dataset for comparative analysis. Extensive experiments demonstrate that our model performs exceptionally well in the high-resolution SAR image generation task.

In the field of synthetic aperture radar (SAR) image analysis, the main challenges include the difficulty of eliminating the effects of ambient noise, the variability of objects, and the distinction between targets and nontargets. To address these issues, we propose a novel detection model, VFMDet, based on brain-inspired visual filtering mechanism. The model comprises two primary components: a brain-inspired filtering module and a SAR ship detection module. The former contains a bottom-up filtering module responsible for low-level feature extraction, an up-bottom filtering module responsible for high-level feature extraction, and a brain-inspired fusion module responsible for fusing the original image with the filtered feature map. In the SAR ship detection process, to accurately regress the orientation of the ship, we introduce a five-point coding scheme in polar coordinate system. Meanwhile, we introduce a Gaussian heatmap strategy (GHS) that utilizes limited covariance and a Gaussian heatmap loss to solve the problem caused by large-scale variation and dense arrangement of ship target. Finally, we design a multitask loss to help the model complete the end-to-end training. We conducted experiments on the rotating SAR ship detection dataset (RSSDD) and the rotating ship detection dataset (RSDD), and the mean average precision (mAP) improved by 0.27% and 0.89%, respectively, compared with the detection results of the best SAR image rotating object detection model.

Video stabilization aims to eliminate random jitter in video sequences, but most methods result in stabilized video with degraded resolution and content loss. In this letter, we propose a full-frame video stabilization algorithm based on motion intent analysis. The algorithm consists of three main steps: motion estimation, motion smoothing, and video completion. First, robust keypoints are extracted using the improved SuperPoint network and refined with the suppression via square covering (SSC) algorithm to obtain stable and reliable keypoints. Then, the Lucas-Kanade algorithm is applied for motion estimation of inter-frame matched feature points. Second, motion smoothing is achieved using the Kalman filtering algorithm to remove the high-frequency jitter component from the trajectory, and motion compensation is applied to the original video sequence to generate a stable image sequence. Finally, to preserve the original video resolution, we propose a video completion method based on motion intent analysis. Experimental results demonstrate that our method achieves higher stability while maintaining the original video resolution compared to the current state-of-the-art video stabilization algorithms.

In the task of Synthetic Aperture Radar (SAR) ship detection, ship targets exhibit arbitrary orientations and are closely aligned. In recent years, oriented bounding box (OBB) based detectors have gained attention as a solution to the significant overlap issue present in horizontal bounding box (HBB) based detectors. Due to the boundary discontinuity issue with current mainstream OBB methods, detectors utilizing the polar coordinate system to define OBB have been introduced. However, currently available methods are usually complicated to encode and decode, and do not take into account the complex background of SAR images. This results in the complexity of neural networks and inaccurate predictions. In this paper, we propose a novelfive-parameter polar coordinate dense regression detector(FPDDet) that only uses a centroid, a mapped polar diameter, and two polar angles to deal with the overlap problem of HBB and the boundary discontinuity problem of OBB. Meanwhile, we introduce a dense regression strategy based on covariance-adaptive rotated Gaussian heatmap to dynamically assign ship target samples in response to large-scale variation of ship targets in SAR images, and we suggest a dense regression heatmap loss function to better match our dense regression strategy. In addition, we design a feature enhancement module to enhance the target features while weakening the background interference, aiming to cope with the severe noise pollution of SAR images. Experimental results show that our FPDDet achieves the state-of-the-art performance on Rotating SAR Ship Detection Dataset (RSSDD) and Rotated Ship Detection Dataset (RSDD). Compared to previous best results, mAP has been improved by 1.2% and 1.71%, respectively.

Ship detection in Synthetic Aperture Radar (SAR) images has been widely applied in the military and civil fields. However, the background environment of SAR images is complex and there are many interferences similar to the ship targets, which is easy to lead fault detection and affect the detection performance. To address this problem, a vision-inspired filtering algorithm (FilterGAN) is proposed to filter out the target-irrelevant information. Firstly, we build a representation model based on filtering mechanism of human brain to guide the design of filtering network. Secondly, to simulate the adjustment process of the priority map reconstruction in human brain, Generative Adversarial Networks (GAN) is used to learn the optimal filtering mapping function. To train FilterGAN, we introduce the labeling process to generate the ground-truth filtered SAR image. Experimental results on AIR-SARShip-1.0 dataset demonstrate that the detection performance of SAR ships can be improved obviously with FilterGAN.

In this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recent years, deep neural networks (DNNs) have made substantial improvements on detection by adopting data augmentation. However, the improvement is limited since the models are sensitive to noise. To address this problem, a Fast Saliency-based Filtering algorithm (FSF) is proposed to filter out interference information. The FSF method is inspired by the filtering mechanisms of the human brain, which help people filter out target-irrelevant information fast to better extract target-relevant information. The FSF includes two parts of the bottom-up process and the top-down process. The bottom-up process is used to extract a saliency map of an input image, and the other one is used to filter out target-irrelevant information based on the saliency map. The FSF can be a front-end preprocessing module of DNNs to fast filter out target-irrelevant information and obtain a primary priority map of an input image. Experimental results demonstrate that our brain-inspired FSF method obtains obvious improvement of detection performance on AIR-SARShip-1.0.