# Material Recognition for Automated Progress Monitoring using Deep Learning Methods

arXiv: https://arxiv.org/abs/2006.16344 · HTML: https://mlsanity.com/paper/2006.16344/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [Construction material classification on imbalanced datasets using Vision Transformer (ViT) architecture](https://mlsanity.com/paper/2108.09527/) (2021)
- [Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach](https://mlsanity.com/paper/2305.15097/) (2023)
- [Semantic segmentation for building houses from wooden cubes](https://mlsanity.com/paper/2503.22125/) (2025)

## Abstract (arXiv)

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.