Aiming at the problems of weak reconstruction performance and robustness in the traditional two-dimensional principal component analysis (2DPCA) algorithm applied to weld surface defect detection,maximizing the projection distance and minimizing the reconstruction error are introduced into the objective function as optimization objectives.And a non-greedy two-dimensional principal component analysis algorithm based on F-norm (non-greedy 2DPCA with F-norm, NG-2DPCA-F) is proposed.This algorithm has good robustness and low reconstruction error.In order to further extract the structural information of the image and obtain the feature matrix with smaller dimension,this paper proposes a bidirectional two-dimensional principal component analysis algorithm based on F-norm (non-greedy bilateral 2DPCA with F-norm,NG-B2DPCA-F).The experiments are carried out with weld surface images with different noise blocks as datasets.The results demonstrate that the proposed algorithm has good robustness in the average reconstruction error,reconstruction image and classification experiments.