计算机工程与应用2025，Vol.61Issue(6)：141-149,9.DOI:10.3778/j.issn.1002-8331.2403-0139
基于改进YOLOv8s的X射线图像违禁品检测算法
X-Ray Image Contraband Detection Based on Improved YOLOv8s
摘要
Abstract
The variable size of contraband in X-ray images and mutual occlusion are the main factors for the low detec-tion accuracy of small model target detection methods,in order to improve the accuracy of contraband detection under the restricted model parameters,an improved small YOLOv8SP contraband detection network is proposed.Aiming at the problem of different sizes of contraband and the difficulty of identifying small targets,a multi-size spatial pyramid pooling module is designed to realize multi-scale feature extraction by using a dense connection method.For the leakage detection problem caused by mutual occlusion of contraband,a parallel attention module is designed to improve the feature extrac-tion ability of occluded objects.A large number of experiments prove that YOLOv8SP achieves 94.27%detection accuracy on the SIXray dataset at a very small scale,which is 2.13 percentage points higher than the original network,and the detection speed is 115 frames per second.It also has obvious advantages in terms of accuracy and computation speed com-pared with similar networks,which proves the effectiveness of the designed algorithm.关键词
X射线/违禁品检测/YOLOv8s/注意力机制/参数受限Key words
X-ray/contraband detection/YOLOv8s/attentional mechanism/parameter constraint分类
信息技术与安全科学引用本文复制引用
颜志明,李新伟,杨艺..基于改进YOLOv8s的X射线图像违禁品检测算法[J].计算机工程与应用,2025,61(6):141-149,9.基金项目
河南省科技攻关项目(232102210040) （232102210040）
河南省高校基本科研业务费专项资金资助(NSFRF220444). （NSFRF220444）
