Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction monitoring has not been an exception; as a part of construction monitoring systems, material classification and recognition have drawn the attention of deep learning and machine vision researchers. However, to create production-ready systems, there is still a long path to cover. Real-world problems such as varying illuminations and reaching acceptable accuracies need to be addressed in order to create robust systems. In this paper, we have addressed these issues and reached a state of the art performance, i.e., 97.35% accuracy rate for this task. Also, a new dataset containing 1231 images of 11 classes taken from several construction sites is gathered and publicly published to help other researchers in this field.
@article{arxiv.2006.16344,
title = {Material Recognition for Automated Progress Monitoring using Deep Learning Methods},
author = {Hadi Mahami and Navid Ghassemi and Mohammad Tayarani Darbandy and Afshin Shoeibi and Sadiq Hussain and Farnad Nasirzadeh and Roohallah Alizadehsani and Darius Nahavandi and Abbas Khosravi and Saeid Nahavandi},
journal= {arXiv preprint arXiv:2006.16344},
year = {2021}
}