An X-Ray Contraband Detection Method Based on Improved YOLOv8

Abstract

X-ray detection of contraband is crucial for public safety; however, it often faces challenges due to cluttered backgrounds and overlapping objects in security inspection images. This study proposes a novel detection framework based on You Only Look Once version 8 (YOLOv8), incorporating three key innovations: multi-scale cross-axis attention (MCA), which captures global dependencies through horizontal and vertical collaborative attention, effectively mitigating irrelevant features in complex X-ray scenarios; a lightweight bottleneck architecture using partial convolution (PConv), which significantly reduces floating point operations (FLOPs) while preserving positional sensitivity; and the focal-enhanced intersection over union (Focaler-IoU) loss function, which dynamically weights difficult samples to enhance regression accuracy. Experiments on the prohibited item detection in the X-ray dataset revealed that our model achieves a mean average precision (IoU = 0.5) ([email protected]) of 97.3%, outperforming YOLOv8s by 1.2 percentage points, and maintains real-time performance of 121 frames per second, surpassing YOLOv10-S (96.5%) and YOLOv12-S (96.8%). Ablation studies highlight the contribution of each module: MCA enhances mAP by 0.7%, PConv decreases FLOPs by 31%, and Focaler-IoU increases precision by 0.9% and recall by 2.4%. The proposed method exhibits substantial potential for real-time security inspections.