A lightweight Xray-YOLO-Mamba model for prohibited item detection in X-ray images using selective state space models

X-ray image-based prohibited item detection plays a crucial role in modern public security systems. Despite significant advancements in deep learning, challenges such as feature extraction, object occlusion, and model complexity remain. Although recent efforts have utilized larger-scale CNNs or ViT-based architectures to enhance accuracy, these approaches incur substantial trade-offs, including prohibitive computational overhead and practical deployment limitations. To address these issues, we propose Xray-YOLO-Mamba, a lightweight model that integrates the YOLO and Mamba architectures. Key innovations include the CResVSS block, which enhances receptive fields and feature representation; the SDConv downsampling block, which minimizes information loss during feature transformation; and the Dysample upsampling block, which improves resolution recovery during reconstruction. Experimental results demonstrate that the proposed model achieves superior performance across three datasets, exhibiting robust performance and excellent generalization ability. Specifically, our model attains mAP 50–95 of 74.6% (CLCXray), 43.9% (OPIXray), and 73.9% (SIXray), while demonstrating lightweight efficiency with 4.3 M parameters and 10.3 GFLOPs. The architecture achieves real-time performance at 95.2 FPS on the GPUs. In summary, Xray-YOLO-Mamba strikes a favorable balance between precision and computational efficiency, demonstrating significant advantages.