Aiming at the problems of aliasing effects and low detection accuracy in scenes with significant intra-class variations generally found in the existing prohibited object detection methods,this paper proposes a prohibited object detection algorithm for X-ray images with reverse weighted fusion of multi-scale features,so as to accurately detect the prohibited object by reverse adaptively guiding the fusion of multi-scale context features.First,a multi-scale scene perception module is used to obtain the object representation information from local and global,which helps to deal with significant intra-class variations.Second,by utilizing the reverse weighted fusion structure,the feature-guided weighting is employed to efficiently fuse multi-level features with rich context features,so as to alleviate the aliasing effects during the fusion process.Finally,a Focal-SIOU loss function is designed to balance the contribution differences between the predicted box of different quality for prohibited objects,and the convergence speed and regression accuracy of predicted box are further improved by combining the angle and side length losses.Extensive experiments were carried out on three very challenging benchmark datasets of SIXray,OPIXray,and PIDray by using the method proposed in this paper,and the mAP reached 93.2%,90.7%,and 85.1%on the three datasets,respectively.The experimental results have fully demonstrated that our method is not only better than the state-of-the-art methods,but also can meet the practical application requirements of real-time object detection.