关键词: 集群目标检测, 深度学习, 特征融合, 海上低空目标, 双通道网络, 注意力机制

Abstract:

In view of the problem of cluster target detection in low-altitude maritime environments， this paper proposed a multi-feature fusion detection network based on gated cross-attention and bidirectional feature pyramid （GCAB-MFF Detector） to address the challenges brought by complex sea conditions， non-Gaussian clutter interference， and the nonlinear characteristics of radar echoes. The network extracted features in the target range dimension and the range-Doppler dimension respectively through a dual-channel architecture and utilized the gated cross-attention module （GCAF） to model cross-modal feature correlations； furthermore， it introduced the bidirectional feature pyramid （BiFPN） to achieve efficient integration of multi-level features through weighted multi-scale fusion and semantic consistency optimization and ultimately output the target existence detection and cluster scale classification results simultaneously. Experimental results indicate that on the self-built dataset， the detection rate of the GCAB-MFF detector reaches 99.92%， and the scale classification accuracy is 92.56%. These results are significantly improved compared to the traditional CFAR method （detection rate < 89%） and traditional classification networks （classification accuracy < 86%）， which verifies its robustness and practicality in low-altitude maritime scenarios.

Key words: cluster target detection, deep learning, feature fusion, low-altitude maritime target, dual-channel network, attention mechanism