Application of Computer Vision Technology in Industrial Automation

Abstract: Computer vision enables machines to acquire, process, and interpret visual information from industrial scenes, and it has become a key enabling technology for modern automation. This paper presents a structured study of the application of computer vision technology in industrial automation, covering automated visual inspection, defect detection, object detection and localization, vision-guided robotics, quality control, and workplace safety monitoring. A conceptual framework is proposed in which industrial cameras capture images on the production line, preprocessing and feature-extraction stages prepare the data, deep-learning models perform inspection and classification, and the results are passed to programmable logic controllers (PLCs) or robotic controllers for real-time action. Deep-learning approaches such as convolutional neural networks, object detectors, and segmentation networks are discussed together with lightweight edge-deployment models that suit the strict latency requirements of production lines.

The study also examines the main practical challenges, including variation in lighting, limited availability of labelled defect images, real-time processing constraints, integration with legacy control systems, and the need for interpretable decisions. Overall, the framework aims to improve inspection accuracy, consistency, and throughput while reducing manual effort and production waste. However, its actual performance needs to be verified through practical implementation and testing on realistic industrial datasets. Future research should evaluate factors such as detection accuracy, false-alarm rate, inference latency, throughput, and robustness under changing factory conditions to determine its suitability for real-world deployment.

Keywords: Computer vision, industrial automation, deep learning, defect detection, machine vision, Industry 4.0, quality inspection.