基于标准数据集的深度学习目标检测算法溯源技术研究OA

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.