Computer Vision–Based Automated Quality Inspection System for Industrial Production Line Optimization

Abstract

The increasing demand for high-quality manufacturing and efficient industrial automation has accelerated the adoption of intelligent inspection technologies in modern production systems. Conventional manual quality inspection methods are often time-consuming, labor-intensive, and susceptible to human error, resulting in reduced production efficiency and inconsistent product quality. This research presents a computer vision–based automated quality inspection system for industrial production line optimization to enhance defect detection accuracy, process efficiency, and real-time decision-making capability. The proposed framework integrates computer vision techniques, machine learning algorithms, high-resolution imaging systems, and industrial automation technologies to enable automated product inspection and quality assessment during manufacturing operations. Image acquisition and preprocessing methods are employed to capture and enhance product images under varying environmental and lighting conditions. Feature extraction and deep learning–based classification models are utilized to identify surface defects, dimensional inconsistencies, structural abnormalities, and manufacturing errors with high precision. The framework further incorporates real-time monitoring and intelligent feedback mechanisms to support adaptive process optimization and reduce production downtime. Performance evaluation is conducted using parameters such as defect detection accuracy, inspection speed, false detection rate, processing efficiency, production throughput, and operational reliability. Experimental analysis demonstrates that the proposed computer vision–based inspection system significantly improves product quality assessment and reduces inspection time compared with conventional manual and rule-based inspection methods. The findings further reveal enhanced adaptability and consistency in handling complex industrial production environments.