To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications,a method has been proposed that employs the Extract-Transf...To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications,a method has been proposed that employs the Extract-Transform-Load(ETL)approach to create an X-ray dataset of contraband items.Initially,X-ray scatter image data is collected and cleaned.Using Kafka message queues and the Elasticsearch(ES)distributed search engine,the data is transmitted in real-time to cloud servers.Subsequently,contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval.The method of integrating targets with backgrounds has enhanced the X-ray contraband image data,increasing the number of positive samples.Finally,an Airport Customs X-ray dataset(ACXray)compatible with customs business scenarios has been constructed,featuring an increased number of positive contraband samples.Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network(Mask R-CNN)algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray(SIXray)and Occluded Prohibited Items X-ray(OPIXray)decreased by 16.3%and 15.1%,respectively,while the ACXray dataset trained algorithm’s accuracy was almost unaffected.This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.展开更多
针对X射线图像违禁品检测中存在的漏检和误检问题,提出基于改进胶囊网络的模型(DMF and SE Capsule)用于X射线图像违禁品检测。该模型在传统胶囊网络的基础上增加了特征增强(dilated convolution multi-scale feature fusion,DMF)模块...针对X射线图像违禁品检测中存在的漏检和误检问题,提出基于改进胶囊网络的模型(DMF and SE Capsule)用于X射线图像违禁品检测。该模型在传统胶囊网络的基础上增加了特征增强(dilated convolution multi-scale feature fusion,DMF)模块和特征筛选(squeeze-andexcitation block,SE)模块。首先使用特征增强模块提取图像特征,通过增加空洞卷积层,并且将所得的高低层语义特征进行拼接融合,从而得到丰富的特征信息;然后再用特征筛选模块,以挤压激励的方式将得到的特征进行筛选;最后再经过网络的胶囊层,从而完成对违禁品的检测。为了验证模型对复杂场景下X射线图像中违禁品的检测能力,在SIXray数据集上进行实验,模型的检测准确率达到79.254%,与原始的胶囊网络(71.350%)相比提升了7.904%,因此,改进模型的检测能力提升明显。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51605069).
文摘To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications,a method has been proposed that employs the Extract-Transform-Load(ETL)approach to create an X-ray dataset of contraband items.Initially,X-ray scatter image data is collected and cleaned.Using Kafka message queues and the Elasticsearch(ES)distributed search engine,the data is transmitted in real-time to cloud servers.Subsequently,contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval.The method of integrating targets with backgrounds has enhanced the X-ray contraband image data,increasing the number of positive samples.Finally,an Airport Customs X-ray dataset(ACXray)compatible with customs business scenarios has been constructed,featuring an increased number of positive contraband samples.Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network(Mask R-CNN)algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray(SIXray)and Occluded Prohibited Items X-ray(OPIXray)decreased by 16.3%and 15.1%,respectively,while the ACXray dataset trained algorithm’s accuracy was almost unaffected.This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.
文摘针对X射线图像违禁品检测中存在的漏检和误检问题,提出基于改进胶囊网络的模型(DMF and SE Capsule)用于X射线图像违禁品检测。该模型在传统胶囊网络的基础上增加了特征增强(dilated convolution multi-scale feature fusion,DMF)模块和特征筛选(squeeze-andexcitation block,SE)模块。首先使用特征增强模块提取图像特征,通过增加空洞卷积层,并且将所得的高低层语义特征进行拼接融合,从而得到丰富的特征信息;然后再用特征筛选模块,以挤压激励的方式将得到的特征进行筛选;最后再经过网络的胶囊层,从而完成对违禁品的检测。为了验证模型对复杂场景下X射线图像中违禁品的检测能力,在SIXray数据集上进行实验,模型的检测准确率达到79.254%,与原始的胶囊网络(71.350%)相比提升了7.904%,因此,改进模型的检测能力提升明显。
