Cargo Segmentation in Stream of Commerce (SoC) X-Ray Images with Deep Learning Algorithms

Inspecting shipping containers using X-ray imagery is critical to safekeeping our borders. One of major tasks of inspecting shipping containers is manifest verification, which has two components: 1) determine what cargos are contained in a shipping container, which can be carried out in cargo segmentation, and 2) compare the cargos in the container with the cargos declared in the manifest. We focus our study on cargo segmentation. Cargo segmentation is the process of partitioning the cargo inside the container into regions with similar appearance. Assign a cargo class label to each pixel in the X-ray images. Our contribution is the development of a deep learning neural net based cargo segmentation algorithm that significantly improves the traditional ways of performing cargo segmentation. The cargo segmentation process is implemented by first partitioning the X-ray images into image tiles of certain sizes, and then train a deep learning (DL) model-based semantic segmentation algorithms using the annotated image tiles to partition the cargo into regions of similar appearance. The DL based semantic segmentation algorithm we used is an encoder-decoder structure often used for semantic segmentation. The DL network implementation chosen for our cargo segmentation is DeepLab v3+, which includes the atrous separable convolution composed of a depthwise convolution and pointwise convolution. Our X-ray cargo images used for development is a government-provided data set (GPD).