Cargo segmentation in stream of commerce (SoC) x-ray images with deep learning algorithms

Weicheng Shen; Jarosław Tuszyński

doi:10.1117/12.2558869

23 April 2020 Cargo segmentation in stream of commerce (SoC) x-ray images with deep learning algorithms

Weicheng Shen, Jarosław Tuszyński

Proceedings Volume 11404, Anomaly Detection and Imaging with X-Rays (ADIX) V; 1140409 (2020) https://doi.org/10.1117/12.2558869
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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 encoderdecoder 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).

Citation Download Citation

Weicheng Shen and Jarosław Tuszyński "Cargo segmentation in stream of commerce (SoC) x-ray images with deep learning algorithms", Proc. SPIE 11404, Anomaly Detection and Imaging with X-Rays (ADIX) V, 1140409 (23 April 2020); https://doi.org/10.1117/12.2558869

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KEYWORDS

Image segmentation

X-ray imaging

X-rays

Image processing algorithms and systems

Algorithm development

Image processing

Convolution

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