Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Construction Instance Segmentation (CIS) Dataset for Deep Learning-Based Computer Vision
Abstract Deep learning-based computer vision (DLBCV) techniques have played an important role in intelligent construction. Image datasets are essential for developing DLBCV algorithms. However, a large-scale construction-specific dataset of major construction elements, such as precast components (PCs), PC delivery trucks, and workers not wearing safety helmets, remains absent. This paper presents the Construction Instance Segmentation (CIS) dataset, a new image dataset aimed at advancing state-of-the-art instance segmentation in the field of construction management. It contains 50,000 images with ten object categories belonging to construction workers, machines, and materials. Two rounds of algorithmic analysis have been conducted to refine and balance the dataset. Finally, a detailed statistical analysis of the dataset is presented.
Highlights A large-scale dataset (CIS) is developed for instance segmentation in construction. The dataset has 50 k images with >100 k annotations. Two rounds of algorithmic analysis were conducted to refine the dataset. A detailed statistical analysis of the dataset is presented.
Construction Instance Segmentation (CIS) Dataset for Deep Learning-Based Computer Vision
Abstract Deep learning-based computer vision (DLBCV) techniques have played an important role in intelligent construction. Image datasets are essential for developing DLBCV algorithms. However, a large-scale construction-specific dataset of major construction elements, such as precast components (PCs), PC delivery trucks, and workers not wearing safety helmets, remains absent. This paper presents the Construction Instance Segmentation (CIS) dataset, a new image dataset aimed at advancing state-of-the-art instance segmentation in the field of construction management. It contains 50,000 images with ten object categories belonging to construction workers, machines, and materials. Two rounds of algorithmic analysis have been conducted to refine and balance the dataset. Finally, a detailed statistical analysis of the dataset is presented.
Highlights A large-scale dataset (CIS) is developed for instance segmentation in construction. The dataset has 50 k images with >100 k annotations. Two rounds of algorithmic analysis were conducted to refine the dataset. A detailed statistical analysis of the dataset is presented.
Construction Instance Segmentation (CIS) Dataset for Deep Learning-Based Computer Vision
Yan, Xuzhong (Autor:in) / Zhang, Hong (Autor:in) / Wu, Yefei (Autor:in) / Lin, Chen (Autor:in) / Liu, Shengwei (Autor:in)
05.09.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Deep learning-based instance segmentation of cracks from shield tunnel lining images
| Taylor & Francis Verlag | 2022