Search Results (80)

Ship detection in synthetic aperture radar (SAR) imagery remains challenging because near-shore clutter, coherent speckle noise, dense scattering responses, and large target-scale variations often obscure vessel boundaries and weaken small-ship signatures. Although single-stage detectors provide efficient inference, their predominantly local convolutional modeling and fixed multiscale fusion strategies are insufficient for capturing long-range sea-surface context and adaptively emphasizing discriminative ship responses. To address these limitations, this paper proposes SeaMamba, a frequency-stabilized selective state-space multiscale detector for SAR ship detection in complex maritime scenes. Specifically, a frequency-domain speckle prior is introduced to stabilize SAR inputs while preserving target localization cues. A bidirectional selective state-space modeling module is then used to propagate long-range contextual information with input-adaptive scanning. Furthermore, a gated pyramid reassembly module is designed to refine multiscale features before dense prediction. The proposed method is evaluated on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID) under a unified five-fold cross-validation protocol. SeaMamba achieved mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5) values of $99.16\pm 0.11\%$ on SSDD and $93.74\pm 0.15\%$ on HRSID. Per-category evaluation, ablation studies, efficiency analysis, and Grad-CAM-based interpretability visualization further demonstrate that SeaMamba improves small-vessel detection, suppresses near-shore false responses, and maintains a practical accuracy-efficiency trade-off. Full article

Deep-learning-based few-shot synthetic aperture radar (SAR) ship detection has demonstrated considerable potential in scenarios with limited annotated samples and dynamically emerging ship categories, closely matching the practical requirements of maritime surveillance and intelligent SAR image interpretation. However, existing methods often overlook challenging negative proposals and the incomplete annotation problem inherent in few-shot learning. In complex offshore and inshore scenes, negative proposals contain either difficult background regions caused by sea clutter, port facilities, and strong scattering interference, or unlabeled ship targets resulting from missing annotations. Existing methods struggle to distinguish between these two types of proposals. Ignoring challenging negatives prevents the detector from learning precise decision boundaries between ships and complex backgrounds, whereas treating unlabeled ships as background introduces erroneous gradients during backpropagation and degrades detection performance. To address these issues, we introduce uncertainty as a measure of proposal reliability and propose two complementary components: uncertainty-guided proposal separation (UGPS) and uncertainty-aware discriminative gradient refocusing (UADGR). UGPS jointly exploits proposal uncertainty and intersection-over-union (IoU) to separate challenging negatives and confusing negatives from the negative proposal set, thereby preserving informative hard backgrounds while identifying potential unlabeled ships. Subsequently, UADGR combines proposal uncertainty with feature dissimilarity to a background prototype to adaptively regulate their training gradients. Specifically, higher weights are assigned to challenging negatives to improve discrimination between ships and complex background interference, whereas lower weights are assigned to confusing negatives to suppress erroneous supervision introduced by missing ship annotations. Extensive experiments on SRSDD-v1.0 demonstrate consistent improvements over existing few-shot detection approaches across different data splits and shot settings, while additional results on SAR-AIRcraft-1.0 further confirm the generalization of the proposed method. Full article

Under moderate-to-high wind conditions, low-frequency wind direction modulation signals in X-band marine radar images are strongly coupled with wave textures, sea clutter, and blind-zone interference, which degrades wind direction retrieval accuracy. To address this problem, this study proposes a wind direction retrieval method based on two-dimensional dual-tree complex wavelet transform (2D-DTCWT), convolutional sparse coding (CSC), and maximum-energy radial rings. First, 2D-DTCWT is used to suppress wave textures and local noise in the wavelet domain while enhancing low-frequency wind direction modulation signals. Then, K–singular value decomposition (K-SVD) learns the energy distribution characteristics of wind signals, and CSC obtains the spatial response distribution of wind energy in radar images. Finally, the maximum-energy radial ring is adaptively identified, and azimuthal energy statistics within this ring are fitted using a cosine-squared function. The proposed method was evaluated using X-band marine radar data collected during sea trials in the coastal waters of Zhejiang, China. On the 900-sample main validation dataset, the proposed method achieved the highest correlation coefficient (CC) of 0.85 and an overall root mean square error (RMSE) of 4.24°, reducing the RMSE by 43.0% and 66.7% compared with conventional single-curve fitting and extended-bow-heading DWT, respectively. The results demonstrate improved robustness under both upwind and downwind blind-zone conditions. Full article

Deploying robust ship detectors in real-world maritime environments is severely bottlenecked by the dual challenges of strictly constrained computational resources and complex background interferences, such as dense berthing, wake patterns, and SAR speckle. To solve these problems, we propose ARGO-Net, a highly efficient architecture tailored for multi-modal maritime detection. At its core, ARGO-Net extracts physically meaningful and highly discriminative features through three targeted innovations. First, a Background Suppression and Reconstruction Module (BSRM) is developed to mitigate irregular coastal clutter and speckle in the frequency domain, reconstructing resilient spatial representations. Second, to capture the intrinsic morphological properties of ships, the High-Resolution Preserving Feature Network (HRPFN) employs geometry-oriented strip convolutions alongside an adaptive scale mechanism, effectively preserving the structural continuity of elongated hulls across extreme scale variations. Finally, a Semantic–Detail Alignment Fusion (SDAF) module is introduced to resolve cross-level spatial mismatches, ensuring that deep semantic context precisely informs low-level boundary localization. Extensive evaluations on the SeaShips and SSDD benchmarks highlight the exceptional efficiency–accuracy balance of ARGO-Net. With a marginal footprint of merely 2.3 M parameters and 6.9 G FLOPs, ARGO-Net achieves 97.9%/75.4% (mAP@50/mAP@50:95) on SeaShips and 99.5%/79.3% on SSDD. The proposed framework demonstrates that integrating background-aware feature reconstruction with geometry-driven fusion yields state-of-the-art localization precision without compromising lightweight deployability. Full article

Synthetic aperture radar (SAR) ship detection is critical for maritime surveillance; however, accurately identifying targets in complex marine environments remains a persistent challenge due to severe sea clutter and coastal interference. To address the prevalent issues of missed detections and false alarms, this paper proposes a novel deep learning framework named AFN-YOLO (Adaptive Frequency Network–You Only Look Once). Specifically, we propose a C2f_FF (C2f Frequency Fusion) module that dynamically extracts and fuses frequency-domain and spatial-domain information, effectively suppressing background interference and artifacts from nearby buildings. Additionally, a TAS-FPN (Triplet Attention-based Spatial FPN) architecture is integrated to capture multiscale features, significantly improving the detection capability for small and overlapping ship targets. Furthermore, the loss function is optimized to compel the model to focus on salient target features while disregarding irrelevant background data. Extensive experiments on the SAR Ship Detection Dataset (SSDD) and the High-Resolution SAR Images Dataset (HRSID) validate the effectiveness of our approach. Full article

Infrared small target detection remains challenging because small targets, cloud edges, sea reflections, building heat sources, and sensor noise often share similar high-frequency responses. Existing local enhancement methods tend to amplify target-like clutter, whereas global modeling methods may dilute sparse target details. To address this issue, this paper proposes CFGMNet (Cross-Frequency Context-Guided Mamba Network), which reformulates infrared small target detection as a high-frequency candidate verification problem constrained by low-frequency background context. Specifically, the proposed CFG-Mamba module decomposes deep features into low-frequency background components and high-frequency candidate regions. Mamba is applied only to the low-frequency branch to capture long-range background dependencies, and the resulting contextual representation is used to gate high-frequency responses, thereby suppressing target-like clutter without indiscriminately enhancing local details. Furthermore, a local contrast gate, a residual attention decoder, and a decoupled prediction head are introduced to perform local saliency calibration, skip-connection noise filtering, and response verification. Experiments on NUAA-SIRST, NUDT-SIRST, and IRSTD-1K demonstrate that CFGMNet achieves a favorable balance between segmentation accuracy, false alarm suppression, and inference efficiency. In particular, CFGMNet achieves 85.27% mIoU and 93.77% F1 on NUAA-SIRST and obtains the lowest false alarm rate on IRSTD-1K, while reaching 141 FPS under forward-pass-only evaluation on an RTX 4090 GPU. Full article

Detecting weak radar targets in complex sea conditions is inherently challenging due to non-stationary sea clutter and sea spikes. Furthermore, traditional dictionary learning algorithms for clutter suppression suffer from high computational complexity. To address these issues, this paper proposes an efficient sea clutter suppression method cascading Block Coordinate Descent (BCD)-accelerated dictionary learning with Tunable Q-factor Wavelet Transform (TQWT) denoising. During dictionary learning, a BCD strategy replaces global Singular Value Decomposition (SVD) with analytical optimization. Combined with an adaptive soft-thresholding operator, this enables low-complexity joint optimization of dictionary atoms and sparse coefficients, drastically reducing training time. Subsequently, a batch-adaptive Orthogonal Matching Pursuit (OMP) algorithm featuring Gram matrix precomputation and a dual-stop mechanism achieves efficient reconstruction and preliminary cancellation of clutter components. Finally, TQWT is applied to filter out residual non-stationary clutter and noise by leveraging its narrowband feature representation and shift invariance. Experiments on measured radar data from the IPIX database and datasets published by the Journal of Radars demonstrate that the proposed method significantly outperforms traditional K-SVD-based algorithms. Specifically, it improves the average signal-to-clutter-plus-noise ratio (SCNR) by 17.48 dB and requires a total execution time of only 7.99 s, achieving a highly favorable trade-off between suppression performance and computational efficiency. Full article

In the application of unmanned aerial vehicle (UAV) target perception in complex marine environments, the significant cyclostationarity of UAV radar echoes makes it highly suitable for extracting their signatures via cyclic spectral analysis. This method projects the signal onto the cyclic frequency dimension, exploiting the fundamental difference between the periodicity of the UAV’s micro-vibrations and the non-periodic randomness of sea clutter, enabling the effective and reliable extraction of the UAV’s target features. However, the sea-clutter background often masks the UAV signal, making it difficult to identify the target processing unit for cyclic spectral analysis rapidly. Autocorrelation processing excels at rapidly filtering out non-periodic components from the echo signal, thereby preserving and enhancing periodic components. It exploits the correlation between adjacent pulses to suppress slow clutter and enhance the echoes from moving targets, thereby establishing a target range for cyclic spectral analysis. Inspired by this, we first propose a novel method in this paper that innovatively employs autocorrelation-guided cyclic spectral fusion filtering, which effectively mitigates the short-term coherence and non-stationarity characteristics of strong sea-clutter background. Corresponding results with a measured strong sea-clutter background demonstrate that the proposed method effectively suppresses sea clutter and reliably extracts UAV target signals from other maritime targets. Compared with the classic moving target indicator (MTI) and the singular value decomposition (SVD) method, as well as their cascade processing, the proposed method achieves higher gain across various input signal-to-clutter-plus-noise ratios (SCNRs), demonstrating broad applicability and excellent detection performance. Full article

Detecting small ship targets in sea–sky background environments is challenging due to interference from clouds, islands, sea clutter, and the limited spatial information in long-range infrared imagery. To address these issues, this paper proposes a robust detection framework that integrates multi-scale spatial feature enhancement with temporal trajectory analysis. First, a candidate target extraction method based on a multi-scale differential histogram of oriented gradients is introduced. By exploiting gradient distribution differences between targets and surrounding backgrounds, our method effectively enhances target responses while suppressing structured background edges. This response is further fused with a log-spectrum-based saliency map to improve target contrast and reduce clutter. Next, a candidate trajectory extraction algorithm based on inverse optical flow matching is developed to utilize temporal consistency. Optical flow-based grayscale compensation predicts target intensity changes between frames, while Kalman filtering estimates motion states and performs trajectory association. Finally, a multi-feature trajectory filtering strategy is designed, combining motion entropy stability, peak signal-to-noise ratio, and trajectory lifecycle to distinguish true targets from false alarms. Experimental results on eight infrared maritime sequences demonstrate superior performance. The proposed method achieves an average Background Suppression Factor (BSF) of 45.2 and an average Signal-to-Clutter Ratio Gain (SCRG) of 22.3 × 10³, representing a substantial improvement over all baseline algorithms. Receiver Operating Characteristic analysis further confirms a mean detection rate exceeding 90% at a false-alarm rate of 10⁻³ across all sequences, confirming improved detection performance and robustness in complex maritime environments. Full article

Object detection in aerial imagery faces extreme target sparsity and high-intensity environmental interference, causing weak targets to be submerged in background clutter. To address this, we propose a Collaborative Feature Purification Detection Transformer (CFP-DETR), which reconstructs discriminative target representations through a collaborative feature purification mechanism. Specifically, the Global Context Denoising Module (GCDM) first suppresses environmental noise at the semantic level to enhance target saliency. The purified features are then fused across scales through an Adaptive Cross-scale Feature Alignment (ACFA) module, which resolves spatial misalignment that otherwise dilutes small-object features during multi-level interaction. Concurrently, a Fine-Grained Detail Injection Module (FGDIM) recovers shallow high-resolution details and injects them into the semantic flow, compensating for information loss caused by progressive downsampling. Together, these modules denoise, align, and recover features to counteract submergence at different stages. Additionally, an efficient lightweight variant, Efficient Lightweight CFP-DETR (EL-CFP-DETR), reconstructs the backbone with partial convolution and structural re-parameterization to improve efficiency while maintaining competitive detection accuracy. Extensive experiments across five datasets validate the effectiveness of this collaborative design. On the SeaDronesSee dataset, CFP-DETR increases $A{P}_{50}$ and $A{P}_S^{val}$ by 1.64% and 4.03% over the baseline, while EL-CFP-DETR reduces parameters by 18% to 16.4M and GFLOPs by 15% to 48.3, reaching 42.8 FPS. Notably, CFP-DETR achieves an inference speed of 37.72 FPS, a 31.2% improvement over the baseline Real-Time Detection Transformer (RT-DETR). Full article

Unmanned Aerial Vehicle (UAV) remote sensing images provide high-resolution and flexible monitoring data for oil spill detection. To address the high computational cost and low accuracy of traditional models, this study proposes an improved model, YOLOv8m-CGSE. The model replaces standard convolution with Group Shuffle Convolution (GSConv), substitutes the C2f module with SENetV2, and introduces a light-weight Cross-scale Context Fusion Module (CCFM) to enhance multi-scale feature representation while maintaining a lightweight structure. Mosaic augmentation was applied to the marine oil spill dataset, improving mAP50 and mAP50–95 to 85.4% and 62.0%, respectively. Based on YOLOv8m, the proposed YOLOv8m-CGSE achieved mAP50 and mAP50–95 of 91.2% and 73.3%, respectively, improving accuracy while reducing parameters by 16.1% and computational cost by 12.6%. Furthermore, a supplementary vulnerability test on highly deceptive oil-free sea surfaces demonstrated that the proposed model actively suppresses complex background clutter (e.g., ship wakes and wave anomalies), effectively reducing false positive detections from 21 (baseline) to 15. The results demonstrate that the proposed model effectively balances high precision, robustness against visual lookalikes and computational efficiency for real-time marine oil spill monitoring. Full article

To address the severe performance degradation of radar weak target detection induced by dense cluster targets and sea-spike interference in nonhomogeneous sea clutter environments, this paper proposes an enhanced Adaptive Normalized Matched Filter algorithm based on iterative weighting and sample purification (IWP-ANMF). The proposed algorithm establishes a closed-loop iterative detection framework capable of highly sensitive discrimination of anomalous data within the reference window—particularly cluster targets and strong discrete sea spikes that severely distort covariance matrix features—identifying them as “contaminated samples.” During each iteration, target-likelihood statistics are calculated for all reference samples based on the current covariance matrix estimate. Subsequently, an adaptive deep-notch suppression strategy is applied to contaminated samples, such as cluster targets, according to their statistical characteristics, thereby progressively purifying the sample covariance matrix (SCM) estimation. Theoretically, this iterative procedure is rigorously proven to converge to the optimal solution of a robust weighted covariance matrix estimation problem. Comprehensive validations using both Monte Carlo simulations and measured K-distributed sea clutter data demonstrate that, compared to classical ANMF and Generalized Inner Product (GIP) approaches, the proposed algorithm exhibits outstanding robustness and detection performance when confronted with heterogeneous contamination scenarios, especially high-density cluster targets. This method effectively eliminates the blind-zone expansion and performance deterioration caused by the wideband masking of cluster targets, significantly enhancing weak target detection capabilities under complex maritime conditions. Full article

Marine oil spills pose a persistent threat to marine ecosystems and coastal economies, and their rapid and unpredictable spread requires timely and reliable monitoring. In X-band marine radar images, oil slicks usually appear as low-contrast dark targets embedded in heterogeneous sea clutter, making accurate segmentation particularly challenging. To address this problem, this study proposes a training-free two-stage oil slick detection framework that combines an improved Slick Boundary Ratio (SBR) feature with an improved Whale Optimization Algorithm (WOA). First, the improved SBR feature is used to extract the oil slick region of interest (ROI). Then, the improved WOA is employed to determine the global threshold for oil slick segmentation. Experimental results show that the proposed method achieves accurate and spatially coherent oil slick segmentation in complex radar backgrounds, with an Accuracy of 99.36%, a Precision of 85.73%, a Recall of 84.42%, an F1-score of 85.07%, and an Intersection over Union (IoU) of 74.01%. These results indicate that the proposed framework can effectively suppress false positives while maintaining strong detection sensitivity, thereby improving segmentation robustness in low-contrast marine radar scenes. Owing to its training-free design, the proposed method shows potential for shipborne and coastal oil spill monitoring applications. Full article

To mitigate the structural amplification of random false alarms during multi-pulse repetition frequency (Multi-PRF) ambiguity resolution, this paper proposes a general waveform synthesis optimization framework based on Bezout’s Identity and Genetic Algorithm (Bezout-GA). By leveraging Bezout’s Theorem, the framework establishes an analytical mapping between the Greatest Common Divisor (GCD) topology of transmission parameters and system-level false alarm boundaries. It is mathematically demonstrated that the uncontrolled inflation of the Least Common Multiple (LCM) in traditional coprime-based strategies leads to severe “spatial over-issuance” of false alarms, a phenomenon particularly exacerbated in heavy-tailed K-distributed sea clutter. The proposed two-stage hybrid paradigm employs a genetic algorithm for global multi-objective search, followed by local number-theoretic refinement via the Extended Euclidean Algorithm to strictly satisfy hardware constraints. Simulations across X-band and L-band scenarios confirm the framework’s superior spectral generalizability. Results indicate that the Bezout-GA optimized waveform achieves a 4.1-fold reduction in expected false alarm volume at the cost of a negligible 0.1% clear-region sacrifice. Notably, in extreme K-distributed clutter ($\nu =0.1$), the framework reclaims an equivalent signal-to-clutter-and-noise ratio (SCNR) gain of up to 3 dB in the L-band, significantly outperforming traditional coprime and maximum clear-region benchmarks. Overall, this study provides a number-theoretic perspective for analyzing spatial false alarm mechanisms and serves as a methodological reference for future investigations into robust Multi-PRF waveform optimization. Full article

X-band marine radar offers unique advantages for monitoring nearshore oil spills. However, oil films and sea clutter exhibit high pixel intensity overlap in radar images. Traditional threshold segmentation and machine learning methods have certain limitations in terms of feature extraction, Region of Interest (ROI) guidance, threshold optimization adaptability, and unsupervised capabilities. To address these issues, a method of oil film detection for ship radar based on multi-dimensional feature-guided extraction and hybrid optimization search is proposed. By combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering with multidimensional features, this method automatically extracts ROIs under unlabeled conditions, effectively suppressing sea clutter interference. Subsequently, an improved Beaver Behavior Optimizer (BBO) and simulated annealing (SA) hybrid algorithm (BBO-SA) is introduced within the ROIs, along with a designed adaptive temperature update strategy, to achieve coordinated optimization of global and local searches. The experimental results demonstrate that the method described in this paper performs exceptionally well across all evaluation metrics, confirming its accuracy and robustness in oil film detection. It provides a viable technical approach for emergency monitoring of nearshore oil spills. Full article