基于识别模型泛化能力的目标识别算法简析

Analysis of Target Recognition Algorithm Based on Generalization Ability of Recognition Model

目标识别算法朝着智能化和跨域交叉方向发展,为适应场景多样性特点,并能够提升算法泛化能力,需满足无监督条件,从而达到跨域目标识别需求。为满足以上目的,文章重点研究目标识别算法框架和模型构建具体方法,提出以SJDA方法合理设计提高识别模型泛化能力的算法。并使用误差边界估计方法验证性能,证明此种算法的合理性以及高泛化能力,得出目标识别算法符合跨域大样本识别需求的结论。

The target recognition algorithm is developing in the direction of intelligence and cross domain intersection.In order to adapt to the characteristics of scene diversity and improve the generalization ability of the algorithm,it needs to meet the unsupervised condition,so as to meet the needs of cross domain target recognition.Based on this background,this paper focuses on the research of target recognition algorithm framework and specific method of model construction,and proposes a reasonable algorithm to improve the generalization ability of recognition model with SJDA method.Through performance verification and result analysis,the rationality and high generalization ability of this algorithm are proved,and the conclusion that the target recognition algorithm meets the needs of cross domain large sample recognition is drawn.