Underwater object detection has emerged as a pivotal technological challenge, playing an indispensable role in various application fields such as underwater structure maintenance, marine ecological protection, and ocean engineering projects. However, in practical application scenarios, underwater images are often complex and variable, with target objects typically being small in size. In light of this, this paper optimizes the general lightweight object detection model RT-DETR, addressing the issue of poor detection performance for small targets on traditional detection layers P3, P4, and P5. Traditional methods tend to add a P2 detection layer to enhance the detection capability for small targets, but this also introduces a series of problems, such as increased computational burden and longer post-processing time. Therefore, there is an urgent need to develop an effective feature pyramid structure specifically tailored for small targets. Based on the original Continuous Convolutional Feature Fusion (CCFF), this paper proposes an improved Multi-scale Feature Enhancement Pyramid (MFEP). Unlike the traditional approach of adding a P2 detection layer, we utilize the P2 feature layer to extract features rich in small target information through Spatial Pyramid Depthwise Convolution (SPDConv) and fuse them with the P3 and P4 layers. Subsequently, the fused feature maps undergo processing by a meticulously designed Small Object Enhancement Module. Experimental results on the DUO and RUOD datasets demonstrate that the proposed model exhibits excellent performance in underwater small object detection.