Open source deep learning models for accurate fastener defect inspection

Abstract

In fastener production, ensuring consistent product quality is essential to protect the safety and performance of components used in demanding applications. Although mechanical sorting equipment and early generations of machine-vision technology have successfully handled dimensional and structural checks, the reliable identification of surface flaws remains difficult. Recent progress in inspection technologies, especially the combination of Artificial Intelligence (AI), Deep Learning (DL), and advanced vision systems, has opened the door to highly accurate, cost-effective surface-defect detection that can outperform human inspectors. Despite these advances, most AI/DL models are designed for general use and still require substantial application-specific training and tuning, which slows large-scale deployment and leads to high costs. The present work investigates the possibility of adopting open source DL models, specifically Transformer-based architectures and Convolutional Neural Networks (CNNs), to classify fastener component images into defective or non-defective, and to segment defective ones, in order to enhance detection localization and to support continuous, high-volume quality-control processes in a cheaper, flexible and fully modifiable way. Our findings indicate that the built-in inductive biases of CNNs continue to offer a significant edge. As a result, CNNs, being generally simpler to train, are well-suited for practical industrial applications where computational resources and latency are key concerns. At the same time, Transformer-based models can serve as complementary approaches, with their effectiveness varying based on the dataset’s properties and the type of defects involved.