AI-Based Automated Defective Exhibit Identification System for Galleries
Abstract
The preservation of cultural heritage exhibits such as paintings, sculptures, and historical artifacts is a critical task for museums and galleries. Over time, these exhibits may suffer from cracks, discoloration, surface erosion, or structural damage due to environmental exposure and human interaction. Traditional inspection methods rely on manual observation by experts, which is time-consuming, subjective, and often unable to detect early-stage defects. This paper presents an AI-Based Automated Defective Exhibit Identification System for Galleries, developed as an Android application using Java/XML and Firebase Realtime Database. The system allows gallery staff to upload baseline reference images of exhibits and later compare them with newly captured images using AI-based computer vision techniques. The comparison process identifies visual deviations that indicate possible defects and automatically records them with exhibit details, timestamps, and defect type. Detected issues are logged in real time, and notifications are sent to administrators for timely review and restoration tracking. Experimental evaluation shows improved detection accuracy, reduced inspection effort, and faster maintenance response. The proposed system offers a scalable, efficient, and cost-effective solution for intelligent gallery management and digital heritage preservation.
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