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Automatic target recognition and detection in synthetic aperture radar (SAR) images is playing a significant role in military and civilian fields. Traditional methods are weak in migration capabilities and cumbersome in manual design. Existing deep learning-based methods can achieve high recognition and detection accuracy, but most of them have a very large number of parameters and slow recognition and computational efficiency, which makes it difficult for them to migrate to edge computing equipments, and computational efficiency is crucial in rapid maritime rescue and emergency military deployment. Therefore, we propose a lightweight feature extraction block, lightweight split concat block (LSCB), for SAR automatic target recognition and detection. Specifically, LSCB consists of several operators, such as split, depthwise convolution (DC), multiple kernel sizes block (MKSB), and channel-shuffle (CS). MKSB is a novel channel attention mechanism designed by us, which can make feature maps have target features of different scales and adjust their receptive field sizes adaptively. In addition, to assess the performance of LSCB, we use LSCB to build LSCNet-R and LSCNet-D for SAR target recognition and detection, respectively, and conduct extensive experiments on MSTAR, SSDD, and SAR-Ship-Dataset. The experimental results show that our LSCNet-R and LSCNet-D built with LSCB achieve higher recognition and detection accuracy and computational efficiency with few parameters compared with existing excellent methods. This further illustrates that the proposed LSCB can extract fine and stable features with few parameters.

LSCB: a lightweight feature extraction block for SAR automatic target recognition and detection