信息通信技术与政策2026，Vol.52Issue(5)：50-57,8.DOI:10.12267/j.issn.2096-5931.2026.05.007
基于标准数据集的深度学习目标检测算法溯源技术研究
Research on traceability techniques for deep learning algorithms based on standard datasets
摘要
Abstract
To address the challenge of lacking effective metrological evaluation methods for the output values of deep learning object detection algorithms due to their lack of interpretability,this study constructs a technical framework for algorithm traceability from a metrological perspective.Using ship detection in Synthetic Aperture Radar(SAR)images as a typical application scenario,the definition and uncertainty sources of algorithm traceability are clarified,and a traceability technical path based on standard datasets together with a continuous comparison chain is proposed.To meet the metrological requirements of algorithm traceability,a quality evaluation indicator system and standardized testing methods for standard datasets are established.This study has achieved quantitative evaluation and traceability of the performance metrics generated by SAR image-based ship detection algorithms.It provides a benchmark for the reliable evaluation of artificial intelligence algorithms and is of great significance for advancing the development of a standardized evaluation system.关键词
算法溯源/标准数据集/深度学习/质量评估Key words
algorithm traceability/standard datasets/deep learning/quality assessment分类
信息技术与安全科学引用本文复制引用
胡天洋,孙小强,陈龙泉,张大元..基于标准数据集的深度学习目标检测算法溯源技术研究[J].信息通信技术与政策,2026,52(5):50-57,8.基金项目
国家重点研发计划项目(No.2022YFF0605903) （No.2022YFF0605903）
