ECMNet: An Efficient CNN–Mamba Network for Maritime Radar Target Detection

Accurate maritime radar target detection plays a critical role in ensuring maritime surveillance and navigation safety. However, reliable detection in sea clutter environments remains challenging due to the strong non-stationarity of clutter and the low signal-to-clutter ratio (SCR) of small targets. Although convolutional neural networks (CNNs) capture local spatial features from time–frequency (TF) representations and Transformers model long-range temporal dependencies, their combined use for target detection often leads to excessive computational cost. To address this issue, we propose an Efficient CNN–Mamba Network (ECMNet), a hybrid architecture that integrates CNN-based local feature extraction with Mamba-based global sequence modeling. Specifically, a 3-layer lightweight CNN module captures discriminative local spatial structures, while a Mamba-based module, composed of four Mamba blocks, models long-range temporal dependencies via selective state-space modeling with linear time complexity. By jointly leveraging both components, ECMNet achieves effective local–global feature modeling while maintaining lower computational efficiency, improving detection performance in complex maritime scenarios. Extensive experiments on the IPIX maritime radar datasets demonstrate that ECMNet consistently outperforms state-of-the-art (SOTA) methods, achieving detection probabilities exceeding 90% on 38 datasets at a false alarm rate (FAR) of 10^-3. Furthermore, compared with SOTA approaches, ECMNet not only achieves superior detection performance but also demonstrates enhanced computational efficiency in terms of parameter scale, computational complexity, and inference latency.