Application of Advanced Computer Vision Techniques for Automated Quality Control in Manufacturing Processes

Abstract

Automated quality control has become essential for modern manufacturing systems as industries transition toward intelligent, autonomous, and data-driven production. Traditional manual inspection suffers from human error, fatigue, and limited repeatability, making it inadequate for high-precision or large- volume manufacturing. Advanced computer vision technologies, particularly those enabled by deep learning and multi-sensor integration, provide a scalable alternative capable of detecting micro-defects, dimensional inaccuracies, assembly faults, and surface anomalies in real time. This study presents an extensive investigation into the deployment of convolutional neural networks, transformer-based visual models, multispectral imaging, and industrial edge-AI systems for automated inspection across diverse manufacturing settings. Field implementation across multiple production lines demonstrates substantial improvements in accuracy, processing speed, and defect classification consistency. The results reinforce the role of machine vision as a cornerstone of Industry 4.0 and highlight the technological, environmental, and infrastructural factors influencing adoption. The study further evaluates operational challenges such as lighting variability, domain shift, dataset imbalance, and hardware constraints, offering strategies for building robust and scalable inspection pipelines. The findings provide a strong foundation for manufacturers seeking cost-effective and high-reliability automated quality control solutions.