摘要： 水下生物目标检测普遍仍采用人工识别的方法，面临着智能化水平低的问题。现有的目标检测算法YOLO系列，存在参数量和计算量大、检测精度差等问题。提出了一种基于RT-DETR模型的改进算法。提出DynaShareNet主干网络，共享stem信息架构以提升特征融合效率并降低计算负担；引入扩张变换器注意块DTAB，结合全局与局部特征交互以增强复杂水下环境鲁棒性；采用MaSA-RetBlock模块，解决目标模糊和低对比度识别问题；以及引入EMASlideVarifocalLoss用于提升难分类目标处理能力。在URPC2020数据集上的试验结果表明，改进算法显著提升了检测精度，mAP50和mAP50:95分别提高3.3%和3.5%，大幅降低了模型复杂度，参数量和计算量分别下降41.7%和47.7%，检测精度和参数量、计算量显著优于YOLO系列算法，同时在RUOD数据集上验证了其良好的泛化性能。研究表明，该改进算法有效提升了水下目标检测的性能与效率，具有较好的应用前景。

关键词: 水下目标检测, 特征融合, RT-DETR, 轻量化网络设计

Abstract: Underwater biological target detection still predominantly relies on manual identification methods, facing challenges related to low levels of intelligence. Existing target detection algorithms, such as the YOLO series, suffer from issues such as large parameter counts, high computational requirements, and poor detection accuracy. This paper proposes an improved algorithm based on the RT-DETR model. The DynaShareNet backbone network is introduced, which shares stem information architecture to enhance feature fusion efficiency and reduce computational burden; the Dilated Transformer Attention Block (DTAB) is introduced to combine global and local feature interactions to enhance robustness in complex underwater environments; the MaSA-RetBlock module is adopted to address target blurring and low-contrast recognition issues; and the EMASlideVarifocalLoss is introduced to enhance the ability to handle difficult-to-classify targets. Experimental results on the URPC2020 dataset demonstrate that the improved algorithm significantly enhances detection accuracy, with mAP50 and mAP50:95 improving by 3.3% and 3.5%, respectively, while significantly reducing model complexity, with parameter counts and computational costs decreasing by 41.7% and 47.7%, respectively. The detection accuracy and parameter count/computational complexity outperform YOLO series algorithms, and the algorithm demonstrates excellent generalization performance on the RUOD dataset. The study indicates that the improved algorithm effectively enhances the performance and efficiency of underwater target detection, offering promising application prospects.

Key words: Underwater object detection, Feature Fusion, RT-DETR, Lightweight network design