A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Computer Vision on X-Ray Data in Industrial Production and Security Applications : A Comprehensive Survey (2023)

Rafiei, M., Raitoharju, J., & Iosifidis, A. (2023). Computer Vision on X-Ray Data in Industrial Production and Security Applications : A Comprehensive Survey. IEEE Access, 11, 2445-2477. https://doi.org/10.1109/access.2023.3234187

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Rafiei, Mehdi; Raitoharju, Jenni; Iosifidis, Alexandros

Lehti tai sarja: IEEE Access

eISSN: 2169-3536

Julkaisuvuosi: 2023

Volyymi: 11

Artikkelin sivunumerot: 2445-2477

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/access.2023.3234187

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/84993

Tiivistelmä

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

YSO-asiasanat: konenäkö; syväoppiminen; röntgensäteily

Vapaat asiasanat: computer vision; deep learning; X-ray; industrial applications; security applications

Liittyvät organisaatiot

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2023

JUFO-taso: 1