Existing underwater small object detection methods are primarily based on deep learning algorithms， which face challenges in balancing lightweight design and detection accuracy， so that they unable to meet the requirements of real-time and resource-constrained platforms. Therefore， Graph-DETR， a lightweight underwater small object detection model based on RT-DETR （Real-Time DEtection TRansformer） and a graph Transformer， was proposed. The model used a lightweight MobileNetV4 backbone improved with the Large Separable Kernel Attention mechanism （LSKAttention） and the Context-Mixing dynamic convolutional block （CM block） to enhance feature extraction efficiency and reduce model complexity. Additionally， a hierarchical Graph Transformer Feature Pyramid Network （GTFPN） was proposed to strengthen multi-scale feature fusion， and the hybrid encoder was optimized via Wavelet Transform Convolution （WTConv）， Adaptive downsampling （Adown）， and path pruning， thereby achieving convolutional receptive field expansion of the CNN-based Cross-scale Feature Fusion （CCFF） module with low parameterization. Experimental results on the underwater public dataset URPC2020 show that， compared to RT-DETR， Graph-DETR reduces the parameters by 66.9% and the reasoning latency by 6.8 ms， achieving a mean Average Precision （mAP） of 53.2% and an Average Precision of 86.8% at an IoU threshold of 0.5 （AP@0.5）； on URPC2021， it has 81.3% recall， 54.1% mAP， 87.6% AP@0.5 with only 10.5 ms latency， outperforming the existing methods. Graph-DETR exhibits excellent performance in underwater small object detection and is practical for deployment on resource-constrained underwater platforms.