 This paper proposes a Novel Lightweight Object Detection Framework for Radar Ship Detection called Multiple Hybrid Attention Ship Detector, MAST. This framework utilizes multiple Hybrid Attention Modules to reduce the complexity while maintaining detection precision. First, a Hybrid Attention Residual Module, HARM, is used to extract features quickly and accurately from the SAR image. Additionally, a Parallel Self-Attention Mechanism is employed to ensure high detection accuracy. Second, an Attention-Based Feature Fusion Scheme, AFFS, is introduced to combine the features extracted from HARM and the Parallel Self-Attention Mechanism. Finally, a new Hybrid Attention Feature Fusion Module, HAFFM, is constructed to fuse the features from HARM and AFFS. Experimental results show that MAST outperforms existing methods in terms of both detection speed and precision. This article was authored by Nan Jingyu, Hao Houran, Tianmin Deng, and others. We are article.tv, links in the description below.