IFIFusion: A independent feature information fusion model for surface defect detection

Abstract

Existing surface-defect detection networks often rely on pre-trained classification models as a backbone to extract multi-scale features and establish decoded modules tailored for these features. However, since the classification model primarily captures identifiable class features in natural images, these decoded modules struggle to accurately delineate the precise spatial structures from the multi-scale feature maps of the backbone. Also, discrete feature contents from different modules lack the necessary fusion, and the architecture lacks the ability to accurately classify the defective object in images, resulting in lower detection performance. Therefore, this paper explored an Independent Feature Information Fusion model (IFIFusion) consisting of spatial multi-axis information fusion, independent classification & spatial information fusion, and multi-scale features fusion for defect detection tasks. This model fully explores the collaborative relationship between independent modules. Firstly, this work designs a Dual-Attention transformer backbone to extract classification features and a Light-Weight multi-scale Network (LWNet) parallel to the backbone to provide independent features for the proposed Spatial multi-Axis Fusion module (SAF). This study provides an independent feature fusion module (IFF), primarily consisting of the connector, convolution layers, batch normalization, ReLU, and interpolation method, to enrich discrete feature contents. Then, the SAF aggregates and fuses the discriminative spatial features from non-classification feature maps. Lastly, the paper fuses the feature maps at each scale to achieve effective coupling and interaction, enhancing the presentation of defective features. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art algorithms regarding visual quality and quantitative evaluations. The code is available at https://github.com/zhx-hub/IFIFusion/tree/main.