Enhanced Product Defect Detection in Smart Manufacturing Using ConvNeXt-Stacked Autoencoder Architecture

Abstract

In the era of Industry 4.0, ensuring product quality through accurate and efficient defect detection has become essential because traditional manual inspection methods are often time-consuming and prone to inconsistency. This study aims to enhance defect detection in smart manufacturing by proposing a hybrid deep learning architecture that combines ConvNeXt and stacked autoencoders. The method leverages ConvNeXt for robust feature extraction and stacked autoencoders for efficient data reconstruction and classification. The model was trained and tested on real-world industrial image datasets involving damaged and intact packaging. Results demonstrate that the proposed method outperforms conventional convolutional neural networks in both detection accuracy and processing efficiency because of its ability to extract deep spatial and semantic features. This research contributes to the advancement of autonomous quality control systems in smart manufacturing. Its impact lies in reducing human dependency, improving inspection accuracy, and fostering the development of intelligent, self-regulating production lines.