Material Recognition for Automated Progress Monitoring using Deep Learning Methods

Navid Ghassemi, Hadi Mahami, Mohammad Tayarani Darbandi, Afshin Shoeibi, Sadiq Hussain, Farnad Nasirzadeh, Roohallah Alizadehsani, Darius Nahavandi, Abbas Khosravi, Saeid Nahavandi

Published 2020-06-29Version 1

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.