摘要
面向高能同步辐射光源(High Energy Photon Source,HEPS)的高性能像素阵列探测器(HEPS-BPIX4)的数据获取系统(Data Acquisition,DAQ)需满足高实时性要求。通过在线压缩图像数据,可有效降低后续传输与存储的压力。针对传统压缩算法在压缩率和实时性方面的不足,本文提出了一种基于深度学习目标检测的图像数据在线压缩方法。采用端到端的YOLO(You Only Look Once)目标检测算法,对深度学习模型进行高效训练,并验证了其在HEPS-BPIX4 DAQ数据流中实现在线数据压缩的可行性。实验结果表明,该方法的图像数据在线压缩平均压缩比达到5.88。同时,设计了高效的部署方案,并对性能进行了测试,单线程下的压缩处理速率可达GB∙s^(−1)量级。此外,进一步提出了适用于HEPS-BPIX4 DAQ框架的多线程部署方案,以满足更高的压缩性能需求,为缓解HEPS-BPIX4 DAQ系统高带宽图像数据处理压力提供了新思路。
[Background]For the High Energy Photon Source(HEPS)High-Performance Pixel Array Detector(HEPS-BPIX4),the HEPS-BPIX4(High Energy Photon Source-Beijing PIXel4)DAQ data acquisition system must meet high real-time performance requirements.Online compression of image data can significantly reduce the pressure on subsequent data transmission and storage.[Purpose]This study aims to overcome the limitations of traditional compression algorithms in terms of compression ratio and real-time performance by proposing an online image compression method based on deep learning object detection.[Methods]The end-to-end object detection model YOLOv10 was trained on an experimental dataset,and its training performance was tested and evaluated to ensure the model achieved the expected level of accuracy.Subsequently,the model's performance and effectiveness in data compression were tested and analyzed on the Intel Xeon 8462Y+CPU and the NVIDIA A40 GPU.Finally,deployment of the model was optimized within the HEPS-BPIX4 DAQ framework under the multi-threaded scenario,and its practical performance was comparatively evaluated across different GPU platforms.[Results]Experimental evaluations indicate that the proposed method achieves an average compression ratio of 5.88 for online image data.Furthermore,an efficient deployment strategy is devised and validated,achieving a compression processing rate in the GB∙s^(−1)range under single-threaded operation.Building upon this,a multi-threaded deployment framework for the HEPS-BPIX4 DAQ system is developed to fulfill more demanding compression performance requirements.[Conclusions]This research presents a novel approach to mitigate the processing burden imposed by high-bandwidth image data in the HEPS-BPIX4 DAQ system.
作者
肖鹏飞
季筱璐
杨宣政
曹平
XIAO Pengfei;JI Xiaolu;YANG Xuanzheng;CAO Ping(University of Science and Technology of China,Hefei 230026,China;Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China)
出处
《核技术》
北大核心
2025年第5期31-41,共11页
Nuclear Techniques
基金
国家自然科学基金青年科学基金(No.12105299)资助。
关键词
高能同步辐射光源
高性能像素阵列探测器
在线数据获取
目标检测
数据压缩
DAQ系统
High Energy Photon Source(HEPS)
High performance pixel array detector
Online data acquisition
Object detection
Data compression
Data acquisition system
