Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

Ship detection is a critical component of marine resource management and environmental monitoring. Methods based on deep learning have been widely used in target detection. However, there are two obstacles in ship detection: 1) due to the mechanism of SAR imaging, SAR images contain a lot of speckle noise, which affects the accuracy of detection; 2) ships in the image have multiscale characteristics caused by significant differences in ships' sizes and imaging resolutions. As CNNs become deeper, the features of small targets gradually diminish or disappear. Therefore, the network is more inclined to extract the features of large targets, resulting in missed and false detection. To solve the above problems, we propose a one-stage ship detection network for speckle noise and multiscale targets called MLC-Net. Firstly, to mitigate speckle noise, we introduce a simplified morphological denoising method that reduces the computational burden of the algorithm. Secondly, we present a large separable kernel module, LSKM, to expand the receptive field and create a new backbone network, ML-CSPDarknet, addressing multiscale target detection. Finally, we propose Cascade ACSF, which effectively fuses features from the backbone at all levels to enhance network performance further. Experimental results on the SAR ship detection dataset and the high-resolution SAR images dataset demonstrate that the detector achieves state-of-the-art performance, with AP reaching 99.16% and 93.08%, respectively. Furthermore, experiments on a large-scale SAR image show the method has great migration application ability.

Intelligent ship detection based on synthetic aperture radar (SAR) is vital in maritime situational awareness. Deep learning methods have great advantages in SAR ship detection. However, the methods do not strike a balance between lightweight and accuracy. In this article, we propose an end-to-end lightweight SAR target detection algorithm, multi-level Laplacian pyramid denoising network (LPDNet). Firstly, an intelligent denoising method based on the multi-level Laplacian transform is proposed. Through Convolutional Neural Network (CNN)-based threshold suppression, the denoising becomes adaptive to every SAR image via back-propagation and makes the denoising processing supervised. Secondly, channel modeling is proposed to combine the spatial domain and frequency domain information. Multi-dimensional information enhances the detection effect. Thirdly, the Convolutional Block Attention Module (CBAM) is introduced into the feature fusion module of the basic framework (Yolox-tiny) so that different weights are given to each pixel of the feature map to highlight the effective features. Experiments on SSDD and AIR SARShip-1.0 demonstrate that the proposed method achieves 97.14% AP with a speed of 24.68FPS and 92.19% AP with a speed of 23.42FPS, respectively, with only 5.1 M parameters, which verifies the accuracy, efficiency, and lightweight of the proposed method.

Compared to horizontal detection, oriented ship detection provides accurate target localization and refined boundary delineation. However, ship detection in SAR imagery faces significant challenges, including complex backgrounds and densely packed targets. To address these problems, we propose a novel network based on Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), named CNN-GNN-Aware detector (CGA-Det). CGA-Det includes three innovations: (1) A CNN-GNN encode network (CG-Encode Network) that captures local and global relationships to concentrate on the targets’ area in complex densely populated scenes; (2) An adaptive feature fusion module (AFFM) that dynamically selects and integrates features from multi-levels to enhance detection effect; and (3) A spatial-channel awareness head (SCHead) that promotes directional sensitivity by enhancing spatial and channel representation capacity of the detection head. Experiments on the SAR Ship Detection Dataset (RSSDD) and RSDD-SAR (RSDD) demonstrates the state-of-the-art performance of CGA-Det, with 99.19% and 97.20% mAP50, excelling in the complex scenes.

Synthetic aperture radar (SAR) imaging, celebrated for its high resolution, all-weather capability, and day-night operability, is indispensable for maritime applications. However, ship detection in SAR imagery faces significant challenges, including complex backgrounds, densely arranged targets, and large scale variations. To address these issues, we propose a novel framework, Center-Aware SAR Ship Detector (CASS-Det), designed for robust multi-scale and densely packed ship detection. CASS-Det integrates three key innovations: (1) a center enhancement module (CEM) that employs rotational convolution to emphasize ship centers, improving localization while suppressing background interference; (2) a neighbor attention module (NAM) that leverages cross-layer dependencies to refine ship boundaries in densely populated scenes; and (3) a cross-connected feature pyramid network (CC-FPN) that enhances multi-scale feature fusion by integrating shallow and deep features. The proposed model achieves mean Average Precision (mAP) of 99.2%, 93.1%, and 82.1% on the SSDD, HRSID, and LS-SSDD datasets, surpassing the second-best methods by 1.2%, 1.8%, and 1.8%, respectively, which demonstrates its effectiveness.

Synthetic aperture radar (SAR) imaging, celebrated for its high resolution, all-weather capability, and day-night operability, is indispensable for maritime applications. However, ship detection in SAR imagery faces significant challenges, including complex backgrounds, densely arranged targets, and large scale variations. To address these issues, we propose a novel framework, Center-Aware SAR Ship Detector (CASS-Det), designed for robust multi-scale and densely packed ship detection. CASS-Det integrates three key innovations: (1) a center enhancement module (CEM) that employs rotational convolution to emphasize ship centers, improving localization while suppressing background interference; (2) a neighbor attention module (NAM) that leverages cross-layer dependencies to refine ship boundaries in densely populated scenes; and (3) a cross-connected feature pyramid network (CC-FPN) that enhances multi-scale feature fusion by integrating shallow and deep features. Extensive experiments on the SSDD, HRSID, and LS-SSDD-v1.0 datasets demonstrate the state-of-the-art performance of CASS-Det, excelling at detecting multi-scale and densely arranged ships.