AI-based Automated Defective Exhibit Identification System

Industries such as manufacturing, retail, and heritage conservation require precise and dependable defect detection mechanisms to ensure product quality and operational excellence. Traditional manual inspection methods often suffer from inconsistencies, prolonged evaluation times, and susceptibility to human error. This study presents an AI-powered Automated Defective Exhibit Identification System that integrates deep learning and computer vision for real-time defect recognition and classification. The proposed approach leverages Convolutional Neural Networks (CNNs) trained on annotated datasets to detect both surfacelevel and structural flaws. By automating the inspection process, the system significantly reduces human intervention while improving the accuracy and uniformity of defect identification. Furthermore, it supports predictive maintenance by identifying recurring defect trends, thus enabling early corrective measures. Designed to be scalable and adaptable, the system is applicable across a broad range of industrial environments. Experimental evaluations demonstrate notable improvements in quality assurance efficiency, defect detection precision, and overall process reliability. The deployment of this system promises reduced operational expenses, minimized material waste, and enhanced product standards. This research contributes to the advancement of intelligent inspection technologies, offering a practical solution for industries aiming to modernize their defect detection infrastructure.