The growing demand for security screening in public spaces necessitates more efficient and reliable X-ray baggage inspection systems. Current manual interpretation of X-ray images at stations，airports，and stadium venues risks missing prohibited items due to operator fatigue. This method also requires considerable human resources，leading to high operational costs. Intelligent X-ray image detection technology，using computer-assisted verification，enhances security check reliability，improves efficiency，and reduces operational costs. Intelligent X-ray detection methods fall into three categories based on imaging approaches： single view，dual view，and CT scanning. Dual-view systems，adding an orthogonal X-ray image to complement single-view inspection，offer improved detection performance by revealing occluded details. This approach achieves an optimal trade-off between detection capability and cost compared with CT scanners. Research on dual-view X-ray prohibited item detection has focused on leveraging the spatial correspondence between views to enhance model performance. Some studies have adopted a primary-auxiliary view strategy. In this strategy，the auxiliary view enhances feature representation in the primary view，somewhat improving detection accuracy. However，this asymmetric fusion approach only considers predictions from the primary view，which can potentially compromise accuracy in complex scenarios where prohibited items are poorly angled or heavily occluded in the primary view. Other researchers have developed symmetric multiscale architectures to boost detection performance through deep dual-view information fusion. However，these methods employ single-column feature fusion，limiting the exploration of potential correlations between feature columns at different scales. Such a limitation may affect the model’s ability to recognize prohibited items of varying sizes and its overall robustness. The challenge remains in developing more effective dual-view feature fusion strategies. These strategies are needed to address complex screening scenarios and improve detection performance across different sizes of prohibited items.

To tackle this challenge，this study presents a dual-view prohibited item detection model，extending the RT-DETR single-channel architecture to a dual-channel network. In the encoder，features are fed into a dual-view multiscale weighted fusion module. This module enhances feature extraction for different-sized prohibited items through multisize sliding windows and establishes spatial correspondence between dual-view features using a cross-attention mechanism. A gated feature fusion mechanism then integrates dual-channel features to strengthen the encoder’s semantic representation. In the decoder，we introduce a 3D anchor-guided localization fusion module to mitigate occlusion. The core of this approach is a supervised fusion stage where the module dynamically generates a set of 3D anchor boxes and query vectors，guided by a 3D bounding box loss function. This encourages the model to learn a geometrically consistent representation，improving localization accuracy via geometric constraints. Furthermore，a synchronous dual-view data augmentation strategy was designed； by applying consistent geometric transformations to both views，this strategy enriches sample diversity while maintaining their spatial correspondence，further improving model generalization in cluttered environments.

To validate the proposed method’s effectiveness，experiments are conducted using ResNet-50 as the backbone network. The study compares the model against two mainstream detection approaches： Transformer-based models （（denosing DETR （DN-DETR），real-time detection Transformer （RT-DETR）） and YOLO models （YOLO11-X）. Additionally，recent dual-view X-ray prohibited item detection methods YOLO_multi and Trans2Ray are included for comparison. All evaluations are performed on the DvXray dataset. Results show remarkable improvements in mAP₅₀ scores. For the overhead view （OL） view，the proposed method achieves improvements of 2.2%，12.2%，2.1%，7.6%，and 16.7% compared with YOLO11，DN-DETR，RT-DETR，Trans2Ray，and YOLO_multi，respectively. For the side view （SD） view，improvements are 5.7%，18.7%，6.8%，25.1%，and 17.0%，respectively. Analysis of individual category performance across 15 prohibited items reveals that the proposed method achieves superior detection in 12 categories for the OL view and 12 categories for the SD view. Under dual perspective joint statistical results，the method demonstrates optimal performance with an area under the P-R curve of 84.9% and a maximum F1 score of 83.5%. For visual analysis，detection results from three representative image sets were examined. The first set comprises contraband under challenging conditions characterized by cluttered backgrounds and rotation，the second set evaluates the model’s proficiency in detecting small objects，occluded backgrounds，and two contraband instances，and the final set consists of negative samples devoid of prohibited items. These visualizations demonstrate that our proposed model achieves optimal performance. Next，we visualized the attention mechanisms within the encoder. The corresponding visualizations for our proposed model were clearly superior to the baseline model. The experimental outcomes validate the effectiveness of the proposed methodology and its components through comprehensive ablation studies. These results demonstrate that the proposed dual-view detection framework effectively improves prohibited item detection performance in X-ray security screening applications.

This study proposes an improved detection network based on the RT-DETR architecture to enhance contraband detection accuracy in X-ray images，particularly in cluttered environments. The model incorporates a dual-view multiscale weighted fusion module with a multiscale window cross-attention mechanism，strengthening recognition capability for variably sized contraband. A 3D anchor-guided localization fusion module further imposes geometric constraints，providing explicit positional guidance to enhance the decoder’s localization accuracy. Additionally，our dual-view joint decision-making strategy enables comprehensive evaluation of detection performance across dual-view data. Experiments demonstrate the model’s competitive performance and robustness in dual-view X-ray contraband detection. However，the model faces persistent challenges including architectural complexity，excessive parameters，and time-consuming training，primarily from computational overhead induced by the dual-channel structure. Future work will explore lightweight dual-view fusion methods and develop efficient collaborative optimization algorithms to balance computational efficiency with detection performance.