Abstract: To address the issues of extreme aspect ratio and difficult classification of small area defects in the task of aluminum profile surface defect classification， we propose a surface defect classification method named Fusion of Weighted non-local Modules and Auxiliary classifier network （FWACNet）. This method proposes a weighted non-local module and uses the dot product similarity to calculate the similarity of different positions in the feature map space to improve the model’s ability to capture long-distance dependencies and contextual information. Meanwhile， we designed an auxiliary classifier to strengthen the mining ability of details in shallow features by integrating deep and shallow features， taking into account the effect of texture， edge， and other details in shallow features on surface defect classification. Finally， we implement simulation experiments on an open data set of aluminum profile surface defects to validate the efficacy of the proposed FWACNet method. The results show that FWACNet outperforms mainstream classification methods in the task of extreme aspect ratio and difficult classification of small area defects， with a classification accuracy of 95.7%.

Key words: classification of surface defects of aluminum profiles, weighted non-local module, feature fusion, auxiliary classifiers