Deep learning on the production line: A novel lightweight CNN model approach for efficient and fast defect detection

Abstract

This paper presents an optimized lightweight CNN model developed using a unique dataset introduced here for the first time to detect defects in manufacturing processes in a factory. The model performance was analyzed comparatively with widely used large-scale deep learning architectures such as VGG16 and ResNet50. All models were trained on the same original dataset, followed by the same approach in tuning hyperparameters such as learning rate, optimization algorithm, and data augmentation strategies. Performance analyses were conducted using fundamental metrics such as accuracy, precision, and F1 score, along with confusion matrices and randomly selected test images. Our proposed model attained high accuracy while reducing computational cost and significantly shortening training time compared to traditional architectures. The results demonstrate that the proposed CNN model achieves a competitive level of accuracy comparable to large-scale deep learning models while serving as a more suitable alternative for low-power hardware systems.