Automated Threat Objects Detection with Synthetic Data for Real-Time X-ray Baggage Inspection

With the recent surge in threats to public safety, the security focus of several organizations has been moved towards enhanced intelligent screening systems. Conventional X-ray screening, which relies on the human operator is the best use of this technology, allowing for the more accurate identifica...

Full description

Saved in:
Bibliographic Details
Main Authors: Chaturvedi, Kunal, Braytee, Ali, Vishwakarma, Dinesh Kumar, Saqib, Muhammad, Mery, Domingo, Prasad, Mukesh
Format: Conference Proceeding
Language:English
Subjects:
Baggage
Detectors
Neural networks
Object detection
Real-time systems
Robustness
Safety
Screening
Synthetic data
Threat
Training
Training data
X-ray
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the recent surge in threats to public safety, the security focus of several organizations has been moved towards enhanced intelligent screening systems. Conventional X-ray screening, which relies on the human operator is the best use of this technology, allowing for the more accurate identification of potential threats. This paper explores X-ray security imagery by introducing a novel approach that generates realistic synthesized data, which opens up the possibility of using different settings to simulate occlusion, radiopacity, varying textures, and distractors to generate cluttered scenes. The generated synthetic data is effective in the training of deep networks. It allows better generalization on training data to deal with domain adaptation in the real world. The extensive set of experiments in this paper provides evidence for the efficacy of synthetic datasets over human-annotated datasets for automated X-ray security screening. The proposed approach outperforms the state-of-the-art approach for a diverse threat object dataset on mean Average Precision (mAP) of region-based detectors and classification/regression-based detectors.
ISSN:2161-4407
DOI:10.1109/IJCNN52387.2021.9533928