In many military and civilian applications, synthetic aperture radar (SAR) image target detection plays a vital role. However, current methods for SAR target detection generally fail to balance speed and accuracy, thus making it impossible to deploy them to real-world engineering applications. In addition, strong scattering, multiscales, high density, complex background interference, and speckle noise make it remarkably challenging to extract effective target information and disentangle background noise from the target information, ultimately resulting in high missing and false alarm rates. To address these issues, a unified dynamic SAR target detection architecture (DAFDet) with asymptotic fusion enhancement and feature encoding decoupling is proposed in this article. First, a dynamic architecture is constructed by cascading two identical detectors and integrating a designed decision maker. This decision maker can automatically decide the inference route by calculating the difficulty score of an SAR image, which ensures efficient inference speed while achieving high accuracy. Second, an asymptotic fusion enhancement feature pyramid network (AFEFPN) is developed, which can avoid the loss and degradation of target information in multistage transmissions through direct interactions of nonadjacent levels, thereby enhancing the extraction of valid target information. Suppression of background noise is achieved by modeling the importance of different feature channels of the fused features. Finally, a task-oriented decoupled head (TODH) is proposed to boost the localization and classification abilities of the model in complex scenarios. It decouples feature encoding at the source, thus providing task-oriented feature context. Numerous experiments on four widely adopted datasets reveal that DAFDet obtains efficient inference speed and optimal detection accuracy, achieving new state-of-the-art target detection performance. The source code will be provided at https://github....