VFSN: a ResNet Multi-scale Fusion Network for Metal Defect Detection

Abstract

Defect detection is an imperative step in ensuring the quality of steel products. To overcome the problem of low accuracy and poor detection of subtle features in current detection methods, a revised neural network model structure has been proposed. VFSN’s main convolutional layer structure is VGG with residuals, where two convolutional blocks are designed. It’s connected to a spatial pyramid pool (SPP) after reducing the aliasing effects and detecting defect features at various scales by constructing multibranch inputs and multilevel feature overlays and fusions. Research on the open-source data set NEU-DET has shown high-accuracy recognition, with the recognition accuracy rate reaching 75.1% and an average accuracy rate improving by 2.7%. The F1 score improved by 1.3%. There’s a significant improvement in detecting small defects using the proposed network structure.