Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain
Abstract Airport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in te...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Blockchain |
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| Online Access: | https://doi.org/10.1049/blc2.12082 |
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| _version_ | 1850245679896592384 |
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| author | Yuzhou Chen Gang Mao Xue Yang Mingqian Du Hongqing Song |
| author_facet | Yuzhou Chen Gang Mao Xue Yang Mingqian Du Hongqing Song |
| author_sort | Yuzhou Chen |
| collection | DOAJ |
| description | Abstract Airport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos. |
| format | Article |
| id | doaj-art-c1d0d8d126884f01b7b72e10a9800f21 |
| institution | OA Journals |
| issn | 2634-1573 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Blockchain |
| spelling | doaj-art-c1d0d8d126884f01b7b72e10a9800f212025-08-20T01:59:22ZengWileyIET Blockchain2634-15732024-12-014439340610.1049/blc2.12082Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchainYuzhou Chen0Gang Mao1Xue Yang2Mingqian Du3Hongqing Song4The Second Research Institute of CAAC Chengdu City Sichuan Province ChinaThe Second Research Institute of CAAC Chengdu City Sichuan Province ChinaChina Internet Network Information Center Beijing ChinaThe Second Research Institute of CAAC Chengdu City Sichuan Province ChinaThe Second Research Institute of CAAC Chengdu City Sichuan Province ChinaAbstract Airport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.https://doi.org/10.1049/blc2.12082blockchaincomputer visiondeep learningedge computingprivate chain |
| spellingShingle | Yuzhou Chen Gang Mao Xue Yang Mingqian Du Hongqing Song Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain IET Blockchain blockchain computer vision deep learning edge computing private chain |
| title | Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain |
| title_full | Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain |
| title_fullStr | Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain |
| title_full_unstemmed | Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain |
| title_short | Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain |
| title_sort | research on airport baggage anomaly retention detection technology based on machine vision edge computing and blockchain |
| topic | blockchain computer vision deep learning edge computing private chain |
| url | https://doi.org/10.1049/blc2.12082 |
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