Aiming at automatic detection of contraband in security X-ray images is difficult, the EM2Det (Enhanced M2Det) model is constructed using different scale feature proportional balance modules, U-shaped network recursive modules, and residual edge attention modules, which it can further improve the detection performance of the M2Det model. First, considering the high semantic information in the deep layer of the backbone network and the detailed feature information in the shallow layer, the feature fusion enhancement module is designed by referring to the feature pyramid idea to enhance its ability to extract features of different scales in the backbone network. Then, the CBAM (Convolutional Block Attention Module) is used to build a residual edge attention module to focus on effective features and suppress useless background interference. Finally, the model is verified on the SIXray_OD dataset. The experimental results show that each module of the design has different degrees of improvement effects, and the average accuracy of the EM2Det model is 6.4 percentage higher than that of the M2Det model.