融合全局感知与多尺度协同的YOLO-Mamba违禁品检测方法OA

To address the challenges including spatial multi-scale variations,target occlusion,and the high false negative and false positive rates of small contraband objects in X-ray security images,this paper proposes a YOLO-Mamba contraband detection algorithm that integrates global perception and multi-scale collaboration.Using YOLOv10 as the baseline,a gated structure-aware selective block(GSSBlock)-enhanced state-space model is incorporated into the backbone to optimize spatial information modeling and effectively focus on key region features.Additionally,a multi-residual connected pooling(M-RCP)structure is designed to enhance the perception of both global and edge information,improving foreground-background differentiation and mitigating the impact of complex background interference.In the neck,a deep feature fusion pyramid network(DFFPN)is introduced,employing bidirectional cross-scale interactions and multi-level feature fusion to strengthen multi-scale feature perception and reduce false positives and false negatives.A dual-branch depthwise separable convolution(DWConv)fusion module is utilized to extract information from different receptive fields,effectively capturing fine-grained details of small contraband objects while maintaining computational efficiency.The proposed method is trained and evaluated on three public datasets:OPIXray,HIXray,and SIXray,achieving mAP50 scores of 94.0％,82.9％,and 94.6％,with improvements of 5.7,2.7,and 4.6 percentage points over the baseline.Experimental results demonstrate that the proposed approach outperforms various state-of-the-art algorithms while maintaining a lower complexity,effectively balancing detection accuracy and computational efficiency,making it a competitive solution for contraband detection in X-ray security screening.