Artificial Intelligence Based Marble Block Analysis System for Sustainable Green Transformation - Ozkaynak, Ummuhan; Ercan, Sahika; Ispir, Fatma Banu; Gencoghu, Muharrem Tuncay; Tanyildizi, Erkan (Ieee, 2024)
The natural stone industry holds a billion-dollar economy worldwide and is recognized as a significant component of economic development. Marble blocks, a crucial part of this industry, find widespread usage in fields such as architecture, construction, interior decoration, and sculpture. The quality and characteristics of marble blocks are vital for end-users and commercial suppliers. Analyzing these features plays a critical role in industrial processes. Traditional methods of marble block analysis are time-consuming, costly, and sometimes yield subjective results. Therefore, the utilization of technologies like artificial intelligence and machine learning offers a new perspective in industrial applications. The Look Marble project is a comprehensive research and development initiative designed to execute complex quality assessment processes within the marble industry. The project aims to optimize quality classification, pricing, and marketing strategies throughout the entire lifecycle of marble blocks, from production to sales. Evaluating the heterogeneous characteristics of marble blocks for quality classification and formulating more effective marketing strategies are among the main objectives of the project. This study focuses on the automatic detection of cracks on marble surfaces, a significant module of the Look Marble project. Crack detection is a crucial factor influencing the quality of marble blocks, and the development of this technology is deemed capable of enhancing efficiency and improving quality control processes in the industry.
Sürdürülebilir Yeşil Dönüşüm için Yapay Zeka Temelli Mermer Blok Analizi Sistemi
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
Keywords: marble block analysis; artificial intelligence; carbon footprint