Improvised Explosive Device Detection Using CNN With X-Ray Images

The concept of a smart city and its associated services have been extensively explored in terms of innovation development and the application of technological concepts. One of the significant concerns in promoting smart living is the security of personal lives and assets, which are at risk from organized crime and acts of terrorism. A considerable amount of attention is paid to preventing bomb attacks in public places, especially the detection of an Improvised Explosive Device (IED). This research focuses on developing an analysis model that can accurately classify instances of x-ray images of baggage or objects as containing IEDs or not. The model provides an alternative to conventional techniques that fail to detect concealed or hidden devices. For this specific project, sample images are generated by experts to cover a range of cases encountered in operations during the past decade. These images are then used to develop a deep learning model, employing several data augmentation methods to overcome the issue of a limited number of training samples. As compared to a related work that exploits neural networks, the proposed model usually achieves higher accuracy rates for unseen samples, with the best accuracy rate being 0.985. Furthermore, an empirical study is conducted to determine the optimal size of the training set that exhibits good predictive performance. The study reveals that a large training set, apart from using a lot of resources, may not yield the best results as it may indicate overfitting.

Automated summary

The concept of a smart city and its associated services have been extensively studied for innovation development and the application of technological concepts. Security, particularly the detection of Improvised Explosive Devices (IEDs), is a significant concern in promoting smart living. This research focuses on developing a deep learning model to accurately classify x-ray images of baggage or objects as containing IEDs or not, providing an alternative to conventional techniques that fail to detect concealed devices. The proposed model achieves higher accuracy rates for unseen samples compared to similar neural network approaches, with the best accuracy rate being 0.985.