Automatic Threat Detection in Single, Stereo (Two) and Multi View X-Ray Images

Accurate X-ray screening systems are of paramount importance in the present day. Most existing systems predict only on the basis of a single image, which could lead to false positives and false negatives due to limited information present. We implemented several approaches using single, two and multiple X-Ray views to make a reliable and practical model with varying levels of success in threat object detection. These approaches include long-established methods such as Bag of Visual Words (BOVW), 3D Object Recognition, Adaptive Implicit Shape Model and Deep Neural Networks. The approaches took in dual inputs to make more informed predictions. Varying levels of success are obtained in these methods ranging from 73% using BOVW to 87% using Deep CNN. It was observed that, when two views of an object are considered, an improvement of 5% to 15% in performance took place (considering various approaches) compared to a single view.