Small ships in synthetic aperture radar (SAR) images have small dimensions, making them susceptible to interference from sea waves, coastlines, and clutter between ships. To address the problem of low detection accuracy caused by the small scale of vessels and background noise, this paper proposes a high-precision network (Pico-Net). First, a feature selection backbone network is introduced to extract detailed information on small vessels. The background noise influence is removed through feature denoising convolution, and the feature attention spatial pyramid pool is constructed to highlight the contrast between small vessels and the background. Second, a multi-scale feature reuse dynamic fusion network (MRD-FPN) with bidirectional connections was designed to facilitate the acquisition of rich semantic information. Finally, a new loss function ZIoU is constructed by combining the advantages of CIoU, EIoU, and αIoU to effectively constrain the predicted bounding boxes. On the SAR ship detection dataset and the infrared ship detection dataset, Pico-Net achieved AP50−95 of 83.5% and 50.3%, respectively. The experimental results demonstrate that Pico-Net exhibits strong noise resistance, effectively combating background interference, and achieving more precise localization of small vessels, significantly reducing false alarms and missed detections.