Zippers are essential in fashion and beyond. Found on jackets, shoes, and bags, they provide a versatile and practical opening and closing mechanism that seamlessly integrates into everyday use and specialized applications. The quality control of zippers is often still based on a subjective visual inspection by experienced persons. This step is time-consuming, inefficient, and associated with a higher error rate. To address these challenges, the deployment of image processing systems for the automated inspection of zippers is becoming increasingly crucial. To determine whether defects are present, approaches often use architectures that perform local feature extraction. However, there is a risk that global relationships will be overlooked, which must not be ignored in the case of defects. The aim of this study is therefore to improve the detection accuracy of zipper defects by integrating both local and global characteristics into one architecture. To achieve this aim, DualFusionNet is proposed, a dual architecture that combines the advantages of CNN- and Transformer-based feature extraction. The integration of the Adaptive Feature Pyramid Network (AFPN) with a Squeeze-and-Excitation (SE) block facilitates the fusion of features from both architectures, enabling the dual architecture to incorporate local and global relationships for improved classification performance. The efficacy of this approach is evidenced by the attainment of an accuracy of 99.74% and a balanced accuracy of 99.58%. The high accuracy shows that the use of deep learning systems for the automatic inspection of defects in zippers is an effective economic step for companies, as it leads to cost and time savings in production due to the high classification performance. This study provides an overview of the results achieved and other key performance indicators of the architecture used.
Mai et al. (2026) studied this question.