Name
Abstract | ||
|---|---|---|
The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called “Small Metallic Threats” (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image). |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1049/ic.2016.0079 | 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016) |
Keywords | Field | DocType |
Deep Learning,X-ray,Small Metallic Threats,Border Security | Computer vision,Visual inspection,Risk analysis (business),Convolutional neural network,Computer science,Pixel,Artificial intelligence,Deep learning,Machine learning | Journal |
Volume | ISBN | Citations |
abs/1609.02805 | 978-1-78561-400-2 | 1 |
PageRank | References | Authors |
0.48 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Jaccard | 1 | 11 | 2.44 |
| Thomas W. Rogers | 2 | 7 | 1.97 |
| Edward J. Morton | 3 | 1 | 0.48 |
| Lewis D. Griffin | 4 | 381 | 45.96 |