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% and 1.1%, 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.