水下目标的多尺度上下文感知检测模型OACHSSCD

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.