Improved prohibited item detection in double-view X-ray images combined with YOLOv11

To address the issues of insufficient adaptability in cross-view feature fusion and inadequate utilization of complementary information in existing dual-view X-ray security inspection image prohibited item detection methods, this paper proposes an improved dual-view fusion detection method combined with YOLOv11 (Dual View Fusion combined with YOLOv11, DVF-YOLOv11). The proposed method employs a parameter-shared dual-branch YOLOv11 backbone network to extract multi-scale features from the overlook-view and side-view images, respectively. A Cross-View Attention Fusion (CVAF) module is designed to adaptively enhance dual-view features through a cascaded mechanism of channel attention and spatial attention. An adaptive weight prediction network is introduced to dynamically adjust the fusion weights of each view, and is combined with channel compression convolution to form a dual-path fusion strategy. A joint loss function composed of feature preservation loss, complementarity loss, and weight balance loss is further designed to guide the fusion learning process. On the DvXray dataset, the proposed method achieves an mAP50 of 94.02% and an mAP50-95 of 79.41%, improving by 2.99% and 5.29%, respectively, over the single overlook-view baseline. Experimental results demonstrate that the proposed method improves the accuracy and robustness of prohibited item detection in dual-view X-ray security inspection images.