Mohamed N Ahmed* and Fayin Li

IBM Fellow, IBM, India

*Corresponding Author: Mohamed N Ahmed, IBM Fellow, IBM, India.

Received: April 25, 2022; Published: July 18, 2022

Abstract

In this work, we present a novel 2-stage system to detect and classify potentially hazardous objects in CT scans of carry-on luggage. The classification and detection approach consists of two 3D neural networks: Region proposal network (RPN) followed by a 3D shape classification network (SCN). RPN segment an input volume into 2 classes: Threat and background. To reduce the number of false positive regions identified by RPN, connected components labeling and various morphological operation are then applied to filter proposed regions for second stage 3D shape classification using SCN. Experimental results show the effectiveness of the proposed system in detecting various threat objects with high detection rates, while producing low false positives.

Keywords: Deep Learning; Convolution Neural Networks; 3D Convolution; Segmentation; Object Detection and Classification

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Citation

Citation: Mohamed N Ahmed and Fayin Li. “Multistage 3D Shape Detection and Classification Network for Threat Items in CT Volumes of Scanned Luggage". Acta Scientific Computer Sciences 4.8 (2022): 29-34.

Copyright

Copyright: © 2022 Mohamed N Ahmed and Fayin Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.