Nowadays, the intricate nature of SAR ship scenes, coupled with the presence of multi-scale targets, poses a significant challenge in detection accuracy. Furthermore, to reduce the financial outlay on hardware, there is also a considerable challenge in lightweighting the model. In order to resolve the aforementioned concerns, we propose a cross-spatial multi-scale lightweight network, designated as DEPDet. Firstly, a new efficient multi-scale detection backbone network DEMNet is redesigned. To improve the feature extraction capability of the network, a cross-spatial multi-scale convolution (CSMSConv) is designed and a new cross-spatial multi-scale convolution module CSMSC2F is constructed. Meanwhile, we introduce an efficient multi-scale attention module. DEMNet is capable of more effectively extracting information pertaining to multi-scale ships. Moreover, to enhance the fusion of features at diverse scales, we design a new path aggregation feature pyramid network DEPAFPN, which combines deformable convolution and CSMSC2F. Finally, we introduce partial convolution to construct a lightweight detection head module, PCHead, which can be employed to extract spatial features with greater efficiency. The publicly available SAR ship datasets SSDD and HRSID are employed for the purposes of conducting experiments. The mAP obtained 98.2% (+1.4%) and 91.6% (+1.6%), respectively. The F1 obtained 0.950 (+1.7%) and 0.871 (+1.4%), respectively. Concurrently, the Params decreased from 3.2M to 2.1M, a decrease of approximately 34%. The FLOPs decreased from 8.7G to 4.5G, a decrease of approximately 48%. The experimental results indicate that the network achieves an effective balance between detection accuracy and lightweight effect with good generalization and extensibility.

The coherent speckle noise in synthetic aperture radar (SAR) imagery, together with complex sea clutter and large variations in ship target scales, poses significant challenges to accurate and robust ship detection, particularly under strict lightweight constraints required by satellite-borne and airborne platforms. To address this issue, this paper proposes a high-precision lightweight detection network, termed High-Lightweight Net (HLNet), specifically designed for SAR ship detection. The network incorporates a novel multi-scale backbone, Multi-Scale Net (MSNet), which integrates dynamic feature completion and multi-core parallel convolutions to alleviate small-target feature loss and suppress background interference. To further enhance multi-scale feature fusion while reducing model complexity, a lightweight path aggregation feature pyramid network, High-Lightweight Feature Pyramid (HLPAFPN), is introduced by reconstructing fusion pathways and removing redundant channels. In addition, a lightweight detection head, High-Lightweight Head (HLHead), is designed by combining grouped convolutions with distribution focal loss to improve localization robustness under low signal-to-noise ratio conditions. Extensive experiments conducted on the public SSDD and HRSID datasets demonstrate that HLNet achieves mAP₅₀ scores of 98.3% and 91.7%, respectively, with only 0.66 M parameters. Extensive evaluations on the more challenging CSID subset, composed of complex scenes selected from SSDD and HRSID, demonstrate that HLNet attains an mAP₅₀ of 75.9%, outperforming the baseline by 4.3%. These results indicate that HLNet achieves an effective balance between detection accuracy and computational efficiency, making it well-suited for deployment on resource-constrained SAR platforms.