Aiming at the problem of high false recognition rate caused by the complex texture of the strip surface defect

the noise

and uneven illumination

a new strip surface defect identification algorithm is proposed. Firstly

the multi-block local binary patterns（ MB-LBP） feature is improved from the perspective of increasing the neighborhood connection

which alleviates the balance between the retained image details and the noise removal caused by the selected sub-window size in the MB-LBP feature extraction process. Secondly

the improved MB-LBP feature and the histogram of oriented gradient（ HOG） feature are linearly weighted to obtain the fusion feature

which makes up for the shortcomings of the MB-LBP feature without characterizing the defect edge and direction

so as to more fully characterize the complex defect texture. Finally

the locality preserving projection（ LPP）algorithm which improves the global information and the supervised information is used to nonlinearly map the high-dimensional fusion features into the low-dimensional essential feature space

and reduce the influence of the fusion feature redundancy on the classifier recognition rate. The simulation results on NEU dataset show that the proposed algorithm has certain robustness to illumination changes

complex texture and noise

and improves the accuracy of strip surface defect identification by 5. 17 %

in case of signal to noise ratio（ SNR） of 50 d B.