Threat Object Classification in X-ray Images Using Transfer Learning

Automatic classification of threat objects in X-ray images is important because of terrorist incidents happening in every country especially in the Philippines. Manual inspection using X-ray machine is prone to human error due limited amount of time given to the operator to check the baggage. This task is also stressful because there are lots of objects to be identified and needs full attention. Over long period of time, the performance of human inspector decreases and the chance of missed detection increases. As a solution to the problem, this paper used the concept of transfer learning in classification of threat objects. The threat objects used in the experiment consists of 4 classes such as blade, gun, knife and shuriken. The dataset came from the GDXray database, a public database of X-ray images. Experiment results showed that by using the concept of transfer learning with data augmentation and fine-tuning, threat objects can be classified at 99.5% accuracy.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Domingo Mery,et al.  Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  T. P. Breckon,et al.  Improving feature-based object recognition for X-ray baggage security screening using primed visualwords , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[4]  Domingo Mery,et al.  GDXray: The Database of X-ray Images for Nondestructive Testing , 2015, Journal of Nondestructive Evaluation.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Marios Anthimopoulos,et al.  Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis , 2016, IEEE journal of biomedical and health informatics.

[7]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Hua Yang,et al.  Transfer-Learning-Based Online Mura Defect Classification , 2018, IEEE Transactions on Semiconductor Manufacturing.

[9]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Argel A. Bandala,et al.  Object Detection Using Convolutional Neural Networks , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[11]  Toby P. Breckon,et al.  Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[12]  Domingo Mery,et al.  Automated Detection of Threat Objects Using Adapted Implicit Shape Model , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Jian Zhang,et al.  Large-Scale Classification of Cargo Images Using Ensemble of Exemplar-SVMs , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

[14]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[15]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[16]  Adrian Schwaninger,et al.  How Image Based Factors and Human Factors Contribute to Threat Detection Performance in X-Ray Aviation Security Screening , 2008, USAB.