Synthetic Aperture Radar image object detection holds significant application value in both military and civilian domains. However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, making them impractical for real-time deployment on edge computing platforms. To address these challenges, this paper proposes a lightweight SAR object detection method optimized for edge devices. First, we design an efficient backbone network based on inverted residual blocks and the information bottleneck principle, achieving an optimal balance between feature extraction capability and computational resource consumption. Then, a Fast Feature Pyramid Network is constructed to enable efficient multi-scale feature fusion. Finally, we propose a decoupled network-in-network Head, which significantly reduces the computational overhead while maintaining detection accuracy. Experimental results demonstrate that the proposed method achieves comparable detection performance to state-of-the-art YOLO variants while drastically reducing computational complexity (4.4 GFLOP) and parameter count (1.9 M). On edge platforms (Jetson TX2 and Huawei Atlas DK 310), the model achieves real-time inference speeds of 34.2 FPS and 30.7 FPS, respectively, proving its suitability for resource-constrained, real-time SAR object detection scenarios.