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Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

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Abstract

Prompt radiation emitted during accelerator operation poses a significant health risk, necessitating a thorough search and securing of hazardous areas prior to initiation. Currently, manual sweep methods are employed. However, the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators. By leveraging advancements in machine vision technology, the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security. Given the criticality of personal safety for stranded individuals, search and security processes must be sufficiently reliable. To ensure comprehensive coverage, 180° camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range. The YOLOV8 network model was modified to enable the detection of small targets, such as hands and feet, as well as larger targets formed by individuals near the cameras. Furthermore, the system incorporates a pedestrian recognition model that detects human body parts, and an information fusion strategy is used to integrate the detected head, hands, and feet with the identified pedestrians as a cohesive unit. This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment, resulting in a notable improvement in the recall rate. Specifically, recall rates of 0.915 and 0.82 were obtained for Datasets 1 and 2, respectively. Although there was a slight decrease in accuracy, it aligned with the intended purpose of the search-and-secure software design. Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

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Data availability

The data that support the findings of this study are openly available in Science Data Bank at https://cstr.cn/31253.11.sciencedb.j00186.00491 and https://doi.org/10.57760/sciencedb.j00186.00491.

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Acknowledgements

The authors would like to express sincere gratitude to our research team and the laboratory instructors for their invaluable assistance and insightful discussions throughout the course of this study. Special thanks are extended to the China Spallation Neutron Source for generously providing access to the experimental site and the necessary dataset, which greatly facilitated the progress and outcomes of this research.

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Authors and Affiliations

  1. School of Traffic and Transportation, Bei**g Jiaotong University, Bei**g, 100044, China

    Ying-Lin Ma

  2. School of Mechanical, Electronic and Control Engineering, Bei**g Jiaotong University, Bei**g, 100044, China

    Yao Wang & Hong-Mei Shi

  3. Institute of High Energy Physics, Chinese Academy of Sciences, Bei**g, 100049, China

    Hui-Jie Zhang

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  1. Ying-Lin Ma
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ying-Lin Ma, Yao Wang and Hui-Jie Zhang. The first draft of the manuscript was written by Ying-Lin Ma and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yao Wang.

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Ma, YL., Wang, Y., Shi, HM. et al. Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision. NUCL SCI TECH 35, 74 (2024). https://doi.org/10.1007/s41365-024-01435-z

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