Prohibited Item Detection Method of X-Ray Security Inspection Image Based on Improved YOLOv8s
Addressing the challenges of missed and false detections of prohibited items in X-ray security imaging,this study introduces an enhanced model,termed YOLOv8s-BiOG.This model builds upon the foundational YOLOv8s framework by incorporating dynamic convolution module,weighted bidirectional feature pyramid network(BiFPN),and global attention mechanism.The dynamic convolution modules replace select convolutional components in both the backbone and neck networks,facilitating refined local feature analysis of prohibited items and bolstering feature extraction capabilities.Subsequently,the BiFPN enhances the model's feature fusion network,optimizing its proficiency in managing feature integration across various scales.The adoption of a global attention mechanism aims to mitigate feature loss and amplify the model's performance in detecting prohibited items.Experimental evaluations conducted on the SI2Pxray and OPIXray datasets demonstrate notable improvements in detection accuracy for a range of prohibited items.
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