递归CNN模型检测x射线安全图像中的异常检测

R. S. Kumar, S. A, A. Balaji, G. Singh, Ashok Kumar, Manikandaprabu P
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引用次数: 1

摘要

为了解决安检过程中x射线图像识别中的违禁品尺度差异问题,我们对Faster RCNN网络进行了升级,提出了多通道区域建议网络(MCRPN)。利用视觉语义中不同层次卷积特征的互补实现了多层特征提取,并融合了VGG16高层更丰富的语义成分和低层较浅的边缘特征;为了构建多尺度违禁品检测网络,将多尺度候选目标区域映射到相应的特征映射;在多通道中引入扩张卷积,设计了一个多分支扩张卷积模块(DCM)来增加感受野,从而增强不同尺度的特征。在自创建的数据集SIXray OD上,增强算法的平均检测准确率达到84.69%,测试性能比原网络提高6.28%。此外,测试结果表明,改进后的算法的识别精度已提高到相当高的水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image
To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a multi-channel region proposal network (MCRPN). Multi-layer feature extraction is achieved using the complementarily of distinct levels of convolution features in visual semantics, and the richer semantic components of VGG16 high-level layers and the shallower edge features of low-level layers are fused; To construct a multi-scale contraband detection network, the multi-scale candidate target regions are mapped to the corresponding feature maps; dilated convolutions are introduced into the multi-channel, and a multi-branch dilated convolutions module (DCM) is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm achieves an average detection accuracy of 84.69 percent and a test performance improvement of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm's recognition accuracy has been increased to a considerable level.
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