Detection of cargo container loads from X-ray images

Abstract

Over 100 million cargo containers that are declared empty on their manifests are transported globally each year. Human operators can confirm if each is truly empty by physical inspection or by examination of an X-ray image. However, the huge number transported means that confirmation is far from complete. Thus, empty containers offer an opportunity for criminals to smuggle contraband. We report an algorithm for automatically detecting loads in cargo containers from transmission X-ray images. Detection without generation of excessive false positives is complicated by the fabric of the container, container variation, damage, and detritus. The algorithm detects 99.3% of loads in stream-of-commerce date while raising false alarms on 0.7% of actually empty containers. On challenging data, created by image synthesis, we are able to achieve 90% detection of loads with the same size and attenuation as a 1.5 kg cube of cocaine or 1 L of water, while triggering fewer than 1-in-605 or 1-in-197 false alarms respectively, on truly empty containers. The algorithm analyses each small window of the image separately, and detects loads within the window by random forest classification of texture features together with the window coordinates.