Construction material classification on imbalanced datasets using Vision Transformer (ViT) architecture
This research proposes a reliable model for identifying different construction materials with the highest accuracy, which is exploited as an advantageous tool for a wide range of construction applications such as automated progress monitoring. In this study, a novel deep learning architecture called Vision Transformer (ViT) is used for detecting and classifying construction materials. The robustness of the employed method is assessed by utilizing different image datasets. For this purpose, the model is trained and tested on two large imbalanced datasets, namely Construction Material Library (CML) and Building Material Dataset (BMD). A third dataset is also generated by combining CML and BMD to create a more imbalanced dataset and assess the capabilities of the utilized method. The achieved results reveal an accuracy of 100 percent in evaluation metrics such as accuracy, precision, recall rate, and f1-score for each material category of three different datasets. It is believed that the suggested model accomplishes a robust tool for detecting and classifying different material types. To date, a number of studies have attempted to automatically classify a variety of building materials, which still have some errors. This research will address the mentioned shortcoming and proposes a model to detect the material type with higher accuracy. The employed model is also capable of being generalized to different datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementMaterial ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Few-shot $\mathbf{1/a}$ Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance
Over the past decade, previous balanced datasets have been used to advance deep learning algorithms for industrial applications. In urban infrastructures and living environments, damage data mining cannot avoid imbalance…
Anomaly Detectionimage-classificationImage Classificationobject-detection+2One Patch is All You Need: Joint Surface Material Reconstruction and Classification from Minimal Visual Cues
Understanding material surfaces from sparse visual cues is critical for applications in robotics, simulation, and material perception. However, most existing methods rely on dense or full-scene observations, limiting the…
Spatial ReasoningA step towards understanding why classification helps regression
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in practice and when it is beneficial. To do so…
Age EstimationClassificationDepth EstimationregressionMultiple Relations Classification using Imbalanced Predictions Adaptation
The relation classification task assigns the proper semantic relation to a pair of subject and object entities; the task plays a crucial role in various text mining applications, such as knowledge graph construction and …
Classificationgraph constructionRelationRelation Classification+2A Hybrid Approach for Binary Classification of Imbalanced Data
Binary classification with an imbalanced dataset is challenging. Models tend to consider all samples as belonging to the majority class. Although existing solutions such as sampling methods, cost-sensitive methods, and e…
Binary ClassificationClassificationDimensionality ReductionEnsemble Learning