To address the challenges posed by high dynamic background interference, multi-scale target detection, and potential feature loss in sea surface target detection tasks during maritime search and rescue operations, based on the YOLOv8 model, this study proposes an improved TS-YOLOv8 network based on the ideas of feature augmentation and adaptive feature fusion. First, a Transformer-based feature fusion (TFF) module is introbuced based on the Transformer's query mechanism. This module facilitates feature augmentation across various scales by enabling depth information interaction among different feature layers. Second, employing learnable parameters, the network adaptively fuses features from each layer. Third, this paper integrates an almost parameter-free Shuffle Attention mechanism to capture intricate feature details while ensuring network efficiency. Comparison experiments with a variety of mainstream detection algorithms and multiple sets of ablation experiments are carried out on the AFO dataset, the mAP₅₀ of the proposed method reaches 95.14%. Compared with the baseline model, the mAP₅₀ is increased by 5.60 percentage points, the mAP₉₅ is increased by 7.38 percentage points, and the FPS reaches 110 frames/s. Multiple sets of ablation experiments are carried out on the SeaDronesSee dataset, the mAP₅₀ of the proposed method reaches 91.34%. Compared with the baseline model, the mAP₅₀ is increased by 4.47 percentage points, the mAP₉₅ is increased by 5.92 percentage points, and the FPS reaches 106 frames/s. Results indicate that proposed model can fully meet the demanding requirements of maritime search and rescue missions.