In order to realize automatic detection of contraband in X-ray images and to work out troubles of detecting mutual shaded, close and small-target prohibited items, an meliorative detection method on the strength of you only look once（YOLOX） algorithm was presented. Firstly, the spatial attention constructed with large kernel attention was introduced in the lower layer of YOLOX backbone network to extract the long-distance dependence information and texture message of the feature map in the lower layer. Then, the convolution block attention module was inserted in the middle and high layer of YOLOX backbone network to heighten the region of interest information and restrain unnecessary information. The proposed means was experimented on an overt security inspection X-ray dataset, meanwhile, in order to strengthen the robustness of the model, Mosaic data augmentation was used in the first 70 training epoch. The results show that, comparing with the basic model, the improved model put on a small amount of parameters and calculations. Mean average precision increases by 2.45% to 87.88%, and average inference velocity is 58.5 frames/s. This study can provide a salutary reference for automatically immediate detection of prohibited items in X-ray images.