Detection of concealed cars in complex cargo X-ray imagery using Deep Learning.

Background: Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators.

Objective: Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery.

Methods: X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images.

Results: CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected.

Conclusions: We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data.