Although the synthetic aperture radar （SAR） image target detection method utilizing convolutional neural network technology can achieve good detection accuracy， its high model complexity limits its practical application and deployment in military rapid decision-making， maritime emergency rescue， and other fields. Therefore， this paper proposes an ultra lightweight small target detection model for radar images. Firstly， a multi branch efficient layer aggregation module is designed to enhance multi-scale perception and adapt to various resources and computing capabilities of actual devices. Secondly， detail enhancement and shared detection heads are utilized to focus on small target information， thereby reducing false detections caused by sea and land clutter interference. Finally， feature richness-guided pruning and knowledge distillation guided representation learning are employed to further compress the model and enhance performance. The experimental results demonstrate that the network model achieves detection accuracies of 89.0%， 98.1%， 82.5%， 98.6%， and 91.5% on MSAR， SAR-Ship， AIR-SARShip-2.0， SSDD， and HRSID datasets， respectively， with a computational complexity of 4.186 G and a parameter complexity of 0.888 M. The algorithm presented in this paper exhibits good robustness， and the network model can achieve optimal detection speed and accuracy at the minimum volume.