多通道区域建议的多尺度X光安检图像检测

针对安检X光图像检测中的违禁品尺度差异问题，对Faster RCNN网络进行改进，提出一种基于多通道区域建议网络（muiti-channel region proposal network，MCRPN）。考虑到不同层卷积特征在视觉语义上的互补性，进行多层特征提取，融合VGG16高层较丰富的语义特征和低层较浅的边缘特征；修改多通道RPN中的锚框参数，将生成的多尺度候选目标区域分别映射到对应的特征图上，构建多尺度违禁品检测网络；在多通道上引入膨胀卷积，设计一种多分支膨胀卷积模块（dilated convolutions module，DCM），增大感受野，增强不同尺度的特征。将改进的算法在自制数据集SIXray_OD上进行实验，检测的平均精度达到84.69%，测性能较原网络提高了6.28%。实验结果表明，改进算法的识别精度有一定提高。

Multi-scale X-Ray Security Inspection Image Detection with Multi-channel Region Proposal

Aiming at the scale difference problem of prohibited items in X-ray images security inspection, the Faster RCNN network is improved and a multi-channel region proposal network（MCRPN） is designed on the basis of Faster RCNN. Firstly, considering the complementarity of different layers convolutional features in visual semantics, multi-layer feature extraction is introduced to combine richer high-level semantic features and low-level edge features of VGG16. Secondly, the anchor frame parameters in multi-channel RPN are modified. The generated multi-scale candidate regions are mapped to the corresponding feature maps, and a multi-scale contraband detection network is constructed. Finally, dilated convolution is introduced, and a dilated convolution module（DCM） is added to the multi channels to increase the receptive field, and the feature of different scales are enhanced. The improved algorithm is tested on the self-made data set SIXray_OD. The average detection accuracy reaches 84.69%, and the detection performance is improved by 6.28% compared with the original Faster RCNN network. The experimental results show that the recognition accuracy of the proposed algorithm has been improved.