Underwater dynamic targets often display significant blurriness in their forward-looking sonar imagery, accompanied by sparse feature representation. This phenomenon presents several challenges, including disturbances in the trajectories of underwater targets and alterations in target identification throughout the tracking process, thereby complicating the continuous monitoring of moving targets. This research proposes a new framework for underwater acoustic data interpolation and underwater object tracking. Considering the character of underwater acoustic images, a Swin Transformer is integrated in the architecture of the YOLOv5 network; then, an improved Deep Simple Online and Real-time Tracking method is developed. By enlarging the bounding box output generated by the detector and subsequently integrating it into the tracker, the sensing horizon of the tracker is broadened. This strategy enables the extraction of noise features surrounding the target, thereby augmenting the target’s characteristics and improving the stability of the tracking process. The experimental results demonstrate that the proposed method effectively reduces the frequency of changes in target identification numbers, minimizes the occurrence of trajectory interruptions, and decreases the overall percentage of trajectory interruptions. Additionally, it significantly enhances tracking stability, particularly in scenarios involving intersecting target paths and encounters.