华东理工大学学报（自然科学版）2026，Vol.52Issue(2)：276-283,8.DOI:10.14135/j.cnki.1006-3080.20250803001
水下目标的多尺度上下文感知检测模型
Multi-Scale Context-Aware Detection Model for Underwater Target
摘要
Abstract
To address the limitations of traditional models in handling complex underwater environmental noise,large variations in target scale,and the trade-off between model size and accuracy,the MSCA-UODA(Multi-scale Context-Aware Underwater Object Detection Algorithm)was proposed.The model includes a context-enhanced downsampling module,CEADown(Context-Enhanced ADown),which effectively reduces model parameters,captures contextual information efficiently,and mitigates underwater environmental noise.Additionally,it introduces a multi-scale feature extraction module based on dual-path partial connection,named CSP-MSPF(Cross Stage Partial-Multi-scale Partial Feature),and incorporates the SHSA(Single-Head Self-Attention)mechanism to enhance the C2PSA module,thereby improving the model's multi-scale feature extraction capability.Experimental results show that on the URPC2020 and DUO datasets,MSCA-UODA improved mAP50 by 2.0 percentage points and 1.1 percentage points,respectively,compared to the baseline model,while reducing the number of parameters by 12.01%.Its overall performance surpassed that of current mainstream object detection models.关键词
水下目标检测/深度学习/注意力机制/下采样/特征提取Key words
underwater object detection/deep learning/attention mechanism/downsampling/feature extraction分类
信息技术与安全科学引用本文复制引用
王峻韬,郑红,陆元军,徐贤,吴丽娟..水下目标的多尺度上下文感知检测模型[J].华东理工大学学报（自然科学版）,2026,52(2):276-283,8.基金项目
上海市2024年度"科技创新行动计划"(24BC3200500,24BC3200300) （24BC3200500,24BC3200300）
