  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.
