Abstract: Aiming at the problem of leakage and misdetection caused by the high percentage of sample overlapping and occlusion, the difficulty of key feature extraction, and the large background noise in X-ray security images, an adaptive panoramic focusing X-ray image contraband detection algorithm is proposed. Firstly, the foreground feature awareness module is designed to accurately distinguish contraband and background noise by enhancing the edge structure and texture details of the foreground target to improve the accuracy and completeness of feature representation. Then, the multi-path two-dimensional information integration module is constructed by combining the multi-branch structure and dual cross attention mechanism to optimize the feature interaction and fusion in the channel and spatial dimensions, to strengthen the extraction capability of key features, and to effectively suppress the background interference. Finally, a panoramic dynamic focus detection head is constructed, which dynamically adjusts the receptive field through frequency adaptive dilated convolutions to accommodate the feature frequency distribution of small-sized contraband targets, thereby enhancing the model's ability to recognize small targets. Trained and tested on the public datasets SIXray and OPIXray, the mAP@0.5 reaches 93.3% and 92.5%, respectively, outperforming the other compared algorithms. The experimental results show that the proposed model significantly improves the leakage and false detection of contraband in X-ray images with high accuracy and robustness.