X-Ray Object Detection

The project

While postdoc at Duke University from 2017-2019, I led the second and third phases of a major research initiative on automatic threat recognition in X-ray scans of luggage. This was sponsored by the TSA, and under PI Larry Carin.

Results and Publications

We achieved breakthroughs in the first realistic application of Deep Learning to the real-world task of threat recognition in Luggage. Additionally, we advanced the science of real-world threat recognition by proposing a novel scheme for improving threat recognition by doing semi-supervised training with real-world data.

Domain-adaptive, Semi-supervised Threat Recognition

During development of these object detection systems, we discovered a quirk in straightforward use of real-world data to improve threat recognition. The capacity of the convolutional feature extractor was enough to learn the differences between real-world and staged data by unmeaningful signatures such as noise. To overcome this, we developed a training procedure based on Domain Adaptive Neural Networks for semi-supervised threat recognition.


  1. domain_adaptive_paper.png
    Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images
    John B Sigman ,  Gregory P Spell ,  Kevin J Liang , and 1 more author
    In Proc.SPIE , 2020


With the team from Smiths, Inc in Wiesbaden, Germany, we published the following collaboration:


  1. smiths_deep_learning_2017.png
    Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach
    Kevin J Liang ,  Geert Heilmann ,  Christopher Gregory , and 6 more authors
    In Proc.SPIE – Invited Paper , 2018


With Rapiscan, Inc. of Fremont CA, we published the following collaboration:


  1. liang2019toward.jpg
    Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection
    Kevin J Liang ,  John B Sigman ,  Gregory P Spell , and 5 more authors
    Advances in X-ray Analysis, Volume 64, proceedings of the 2020 Denver X-ray Conference, 2019