Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach

Construction progress monitoring (CPM) is essential for effective project management, ensuring on-time and on-budget delivery. Traditional CPM methods often rely on manual inspection and reporting, which are time-consuming and prone to errors. This paper proposes a novel approach for automated CPM using state-of-the-art object detection algorithms. The proposed method leverages e.g. YOLOv8's real-time capabilities and high accuracy to identify and track construction elements within site images and videos. A dataset was created, consisting of various building elements and annotated with relevant objects for training and validation. The performance of the proposed approach was evaluated using standard metrics, such as precision, recall, and F1-score, demonstrating significant improvement over existing methods. The integration of Computer Vision into CPM provides stakeholders with reliable, efficient, and cost-effective means to monitor project progress, facilitating timely decision-making and ultimately contributing to the successful completion of construction projects.

A comparative study of attention mechanism and generative adversarial network in facade damage segmentation

Semantic segmentation profits from deep learning and has shown its possibilities in handling the graphical data from the on-site inspection. As a result, visual damage in the facade images should be detected. Attention mechanism and generative adversarial networks are two of the most popular strategies to improve the quality of semantic segmentation. With specific focuses on these two strategies, this paper adopts U-net, a representative convolutional neural network, as the primary network and presents a comparative study in two steps. First, cell images are utilized to respectively determine the most effective networks among the U-nets with attention mechanism or generative adversarial networks. Subsequently, selected networks from the first test and their combination are applied for facade damage segmentation to investigate the performances of these networks. Besides, the combined effect of the attention mechanism and the generative adversarial network is discovered and discussed.

Fast Crack Detection Using Convolutional Neural Network

To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use.

Crack Semantic Segmentation using the U-Net with Full Attention Strategy

Structures suffer from the emergence of cracks, therefore, crack detection is always an issue with much concern in structural health monitoring. Along with the rapid progress of deep learning technology, image semantic segmentation, an active research field, offers another solution, which is more effective and intelligent, to crack detection Through numerous artificial neural networks have been developed to address the preceding issue, corresponding explorations are never stopped improving the quality of crack detection. This paper presents a novel artificial neural network architecture named Full Attention U-net for image semantic segmentation. The proposed architecture leverages the U-net as the backbone and adopts the Full Attention Strategy, which is a synthesis of the attention mechanism and the outputs from each encoding layer in skip connection. Subject to the hardware in training, the experiments are composed of verification and validation. In verification, 4 networks including U-net, Attention U-net, Advanced Attention U-net, and Full Attention U-net are tested through cell images for a competitive study. With respect to mean intersection-over-unions and clarity of edge identification, the Full Attention U-net performs best in verification, and is hence applied for crack semantic segmentation in validation to demonstrate its effectiveness.