Threat object classification in x-ray images using transfer learning


Gokongwei College of Engineering


Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018

Publication Date



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. © 2018 IEEE.


Digitial Object Identifier (DOI)



Electrical and Computer Engineering | Electrical and Electronics


Transfer learning (Machine learning); X-rays; Computer vision; Neural networks (Computer science)

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