In recent decades, the industrial world has undergone a significant transformation through the inclusion of innovative technologies that enhance manufacturing processes. In this context, Machine Vision inspection systems play a key role in ensuring quality by identifying defects in production. Automated defect detection systems improve productivity by reducing manual interventions, which can be time-consuming and prone to errors. This paper presents DIE-VIS, a real-world implemented visual inspection system for detecting defects in cardboard box manufacturing using traditional Computer Vision techniques. We provide a comprehensive evaluation comparing it to the YOLOv8 state-of-the-art deep learning model, demonstrating how, in the specific application of cardboard manufacturing, customized solutions still offer fundamental advantages.