Due to the unique imaging mechanism of synthetic aperture radar (SAR), existing deep learning detection algorithms struggle to achieve an optimal balance between accuracy and speed. To address the requirements for edge applications, this paper proposes a lightweight SAR image aircraft target detection network, SAERFDnet, which integrates pruning techniques for optimization. Based on YOLOV8n, SAERFDnet utilizes re-parametrized large kernel convolutions for feature extraction, while the neck of the network incorporates an adaptive multiscale discrete feature fusion module, providing a larger effective receptive field with a shallower network depth. Additionally, a deformable convolution is introduced in detection head classification branch to enhance the network′s focus on the geometric feature differences of different target classes. A frequency-adaptive dilation convolution is employed in the regression branch to strengthen the model′s ability to locate targets in high-frequency image regions. Finally, model pruning is applied to further reduce the model size and improve computational efficiency. Experiments conducted on three publicly available datasets demonstrate that the proposed method achieves 96.3% mAP50 and 72.5% mAP50-95 on the SAR-AIRcraft-1.0 dataset, with 0.5M parameters and 2G FLOPS, representing a reduction of 83.3% in parameters and 75.3% in FLOPS compared to the YOLOv8n model, while improving detection accuracy by 0.7% mAP50 and 2.2% mAP50-95. Compared to other models, the proposed method effectively improves detection efficiency in SAR image aircraft target detection while maintaining high detection accuracy. Furthermore, transfer experiments on the SADD dataset and GaoFen-3 aircraft target dataset show that the proposed method exhibits excellent generalization and robustness.