Recently, significant advancements have been achieved in optimizing algorithms for synthetic aperture radar (SAR) ship detection. Nevertheless, two challenges still impede further research: 1) Mainstream methods, whether anchor-free or anchor-based, adhere to a dense paradigm, leading to substantial redundancy and limited adaptability. 2) Ship targets in SAR images exhibit large shape variations and scale differences, making it difficult to efficiently extract key features from background clutter. To tackle the aforementioned problems, we propose DCEA (DETR with Concentrated dEformable Attention), a query-based method for end-to-end optimization of the current pipeline. First, for the irregular shapes and sparse distribution of ships, the concentrated deformable attention (CDA) is introduced to model the spatial positions of targets, simulating their geometric transformations with precision. Second, an attention-wise propagation module (APM) is designed to integrate local fine-grained information with global semantic information, improving the detection performance for objects across diverse scales. Finally, due to the lack of information exchange between object queries, a dimension-wise information mixing (DIM) module is employed to incorporate key information from various dimensions to enhance their representation capability. To validate the superior performance of DCEA, we conduct extensive experiments on multiple public datasets, achieving mean average precision (mAP) scores of 0.991, 0.929, and 0.962 on the SSDD, HRSID, and SAR-Ship-Dataset, respectively, with a model size of only 14.34M parameters and 44.4 GFLOPs.

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably.

Recently, with the increasing adoption of synthetic aperture radar (SAR) ship detection methods on mobile platforms, the lightweighting of detection methods has become a research focus. Despite certain achievements, there are still several limitations: (1) Existing studies have mainly focused on reducing model complexity through shallow network structures. However, this approach frequently results in performance degradation, as they neglected a thorough investigation into achieving a better balance point between inference speed and detection accuracy. (2) Under the lightweight network structure, the rich edge features contained in SAR images, which are crucial for distinguishing ship targets from complex backgrounds, are often underutilized. To address these issues, we propose a novel lightweight detection method based on edge feature aware and fusion. Specifically, to effectively extract edge feature, we introduce an Edge FeatureAware (EFA) network that incorporates a multi-scale channel attention module. Furthermore, a lightweight feature fusion network, Filter-Pruned Bi-directional Feature Pyramid Network (FP-BiFPN), is carefully designed, which can not only suppresses background information but also accentuates ship targets. Finally, we propose a selective quantization algorithm based on a bit-width selection mechanism to reduce model memory usage without compromising performance. To validate the superiority of our proposed method, we conduct extensive experiments on multiple public datasets, achieving average accuracy scores of 94.2%, 97.6%, and 97.7% on the HRSID, SAR-Ship-Dataset, and SSDD, respectively, with a model parameter size of only 3.36M, and the fastest processing time for a single frame is 7.2ms.