Aiming at the large model parameters and slow detection speed encountered by current lightweight target detection algorithms when applied to the task of detecting military aircraft in remote sensing images, this study proposes a lightweight detection algorithm for military aircraft targets based on YOLOv8n, named LeYOLO-MARs. The algorithm introduces an optimized inverted bottleneck module to replace the traditional bottleneck in the backbone network, reducing computational requirements while maintaining feature extraction capabilities and improving processing speed. In the neck network, a fast pyramid architecture is integrated to reduce the number of convolutional layers, enhance the efficiency of semantic information sharing, and decrease lock and wait times, while also considering limited parallelization opportunities and architectural complexity. A lightweight decoupled detection head, simplified through pointwise convolution, is employed, alongside the use of Inner-SIoU as the new localization regression loss function, which enhances the ability to learn from small target samples and accelerates the convergence of bounding box regression. Moreover, the algorithm incorporates a lightweight pyramid compression attention mechanism, effectively combining local and global attention to establish long-range channel dependencies. Experimental results demonstrate that the improved algorithm achieves a detection accuracy of 95.7%, 0.4% higher than the baseline model, while reducing model parameters by 43% and computational load by 63%, marking a notable improvement in detection performance compared to mainstream algorithms and enabling high-quality real-time detection of military aircraft targets.