Synthetic Aperture Radar (SAR) ship detection is vital for maritime traffic monitoring. However, challenges such as speckle noise, complex sea conditions, and variations in ship sizes pose significant obstacles to traditional object detection methods. This paper proposes EEC-DETR, a novel approach that leverages a lightweight backbone network, multi-scale feature fusion, and wavelet convolution to enhance model performance. The EfficientVit backbone adopts a sandwich structure to reduce parameters, while the EIFI module integrates PSConv multi-scale convolutional layers and Efficient additive attention for global modeling. Additionally, the CCAM module combines WTConv wavelet convolution with a CAFM convolutional attention fusion mechanism to strengthen target feature extraction under complex backgrounds. Experimental results on the SSDD, HRSID, and iVision-MRSSD datasets demonstrate that EEC-DETR achieves a 5.5% improvement in AP50, an 8.5% improvement in AP50-90, and a 41.3% reduction in model parameters compared to the baseline RT-DETR model. Here we show that EEC-DETR offers a lightweight and high-performance solution for SAR ship detection, promising practical applications in maritime surveillance and security.