X-ray contraband detection plays an important role in the field of public safety. To solve the multi-scale and obscuration problem in X-ray contraband detection, we propose a material-aware path aggregation network to detect and classify contraband in X-ray baggage images. Based on YoloX, our network integrates two new modules: multi-scale smoothed atrous convolution (SCA) and material-aware coordinate attention modules (MCA). In SAC, an improved receptive field-enhanced network structure is proposed by combining smoothed atrous convolution, using separate shared convolution, with a parallel branching structure, which allows for the acquisition of multi-scale receptive fields while reducing grid effects. In the MCA, we incorporate a spatial coordinate separation material perception module with a coordinated attention mechanism. A material perception module can extract the material information features in X and Y dimensions, respectively, which alleviates the obscuring problem by focusing on the distinctive material characteristics. Finally, we design the shape-decoupled SIoU loss function (SD-SIoU) for the shape characteristics of the X-ray contraband. The category decoupling module and the long–short side decoupling module are integrated to the shape loss. It can effectively balance the effect of the long–short side. We evaluate our approach on the public X-ray contraband SIXray and OPIXray datasets, and the results show that our approach is competitive with other X-ray baggage inspection approaches.

To resolve the problem of occlusion of the depth information of x-ray images and the detection of small-scale contraband in the detection of contraband objects, an improved prohibited item detection network has been proposed based on YOLOX. First, a material-aware atrous convolution module (MACM) is added to the feature pyramid network to enhance the model's multiscale fusion and extraction ability for material information in x-ray image. Second, a spatial pyramid split attention mechanism (SPSA) is proposed to fuse spatial and channel attention for different scale spatial information features. Finally, CutMix data augmentation strategy is adopted to improve the robustness of the model. The overall performance identification experiments were conducted on the publicly available OPIXray dataset. The average accuracy (mean average precision, mAP) of the method is 93.10%. Compared with the baseline model YOLOX, the mAP is improved by 3.25%. The experimental results show that our method achieves stateof-the-art detection accuracy compared with existing methods.