Automated detection of cars in transmission X-ray images of freight containers

We present a method for automated car detection in xraytransmission images of freight containers. A random forest classifier was used to classify image sub-windows as “car” and “non-car” based on image features such as intensity and log-intensity, as well as local structures and symmetries as encoded by Basic Image Features (BIFs) and oriented Basic Image Features (oBIFs). The proposed approach was validated using a dataset of stream of commerce X-ray images. A car detection rate of 100% was achieved while maintaining a false alarm rate of 1.23%. Further reduction in false alarm rate, potentially at the cost of detection rate, was possible by tweaking the classification confidence threshold. This work establishes a framework for the automated classification of X-ray transmission cargo images and their content, paving the way towards the development of tools to assist custom officers faced with an ever increasing number of images to inspect.