复杂背景下多尺度X光违禁品检测

Multi-Scale Detection for X-Ray Prohibited Items in Complex Background

针对安检X光图像中违禁品的自动检测一直存在困难,使用不同尺度的特征比例平衡模块、U型网络递归模块和残差边注意力模块构建EM2Det(Enhanced M2Det)模型,进一步提升M2Det模型的检测性能。首先考虑主干网络深层中的高语义信息和浅层中的细节特征信息,借鉴特征金字塔思想设计特征融合增强模块,加强模型对主干网络中不同尺度特征的提取能力;然后设计8个U型网络递归模块,增强其对基本特征不同水平、不同尺度的细节特征提取能力;接着使用CBAM(Convolutional Block Attention Module)构建残差边注意力模块,使其关注有效特征,抑制无用的背景干扰;最后在SIXray_OD数据集上对模型进行验证。实验结果表明,设计的各个模块均有不同程度的提升效果,EM2Det模型的平均精度比M2Det模型提升6.4个百分点。

Aiming at automatic detection of contraband in security X-ray images is difficult,the EM2Det(Enhanced M2Det)model is constructed using different scale feature proportional balance modules,U-shaped network recursive modules,and residual edge attention modules,which it can further improve the detection performance of the M2Det model.First,considering the high semantic information in the deep layer of the backbone network and the detailed feature information in the shallow layer,the feature fusion enhancement module is designed by referring to the feature pyramid idea to enhance its ability to extract features of different scales in the backbone network.Then,the CBAM(Convolutional Block Attention Module)is used to build a residual edge attention module to focus on effective features and suppress useless background interference.Finally,the model is verified on the SIXray_OD dataset.The experimental results show that each module of the design has different degrees of improvement effects,and the average accuracy of the EM2Det model is 6.4 percentage higher than that of the M2Det model.