In response to the issues of low accuracy and poor real-time performance in the X-ray security inspection scenario, a new lightweight detection method has been proposed based on the YOLOv7-Tiny model. Firstly, a lightweight GhostNetV2 module was introduced into the backbone network to reduce model parameters while improving training efficiency. Secondly, a Pyramid Split Attention mechanism was incorporated into the neck network of YOLOv7-Tiny to effectively address the problem of insufficient feature extraction due to parameter reduction, enhancing accuracy in detecting complex backgrounds and multi-scale objects. Finally, the Normalized Wasserstein Distance method was used to measure the loss, replacing the original Intersection over Union metric, which reduces sensitivity to small object position deviations and enhances accuracy in regressing small objects. Experimental results show that the improved model achieves an average detection accuracy of 92. 9%, 76.2%, and 91.2% on the SIXray, CLCXray, and OPIXray datasets, representing 6. 5% , 2% , and 1. 8% improvement over the original algorithm, respectively. The proposed model, while being lightweight, further enhances detection capabilities and can meet real-time detection requirements, demonstrating significant practical value.