Dual-task guided network: hybrid supervised learning for surface defect detection

Abstract Efficient and accurate surface defect detection is a crucial task for industrial production, where traditional computer vision methods often fail to reliably identify subtle manufacturing defects. This paper proposes a novel hybrid supervised learning-based dual-task guided network (DTGNet) model that combines the strengths of supervised and unsupervised learning techniques to address the limitations of traditional approaches. By integrating partial convolution, scalable convolution and content-guided attention fusion (CGAFusion), our model improves recognition of complex defect features and achieves state-of-the-art performance on benchmark datasets. Experimental validation on the KSDD2 dataset empirically demonstrates DTGNet’s superior performance, with Lite DTGNet achieving a 93.51% accuracy. This innovative approach not only advances industrial quality control technologies but also provides a flexible, data-efficient framework.