Open Access

ARTICLE

Research on Enhanced Contraband Dataset ACXray Based on ETL

by Xueping Song^1,*, Jianming Yang¹, Shuyu Zhang¹, Jicun Zhang^1,2,*

1 School of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, China
2 Neusoft Reach Automotive Technology (Dalian) Co., Ltd., Dalian, 116085, China

* Corresponding Authors: Xueping Song. Email: ; Jicun Zhang. Email:

(This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)

Computers, Materials & Continua 2024, 79(3), 4551-4572. https://doi.org/10.32604/cmc.2024.049446

Received 08 January 2024; Accepted 19 April 2024; Issue published 20 June 2024

Abstract

To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval. The method of integrating targets with backgrounds has enhanced the X-ray contraband image data, increasing the number of positive samples. Finally, an Airport Customs X-ray dataset (ACXray) compatible with customs business scenarios has been constructed, featuring an increased number of positive contraband samples. Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray (SIXray) and Occluded Prohibited Items X-ray (OPIXray) decreased by 16.3% and 15.1%, respectively, while the ACXray dataset trained algorithm’s accuracy was almost unaffected. This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.

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Keywords

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