An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography

Abstract

Conventionally, the number of steel rebars at construction sites is manually counted by workers. However, this practice gives rise to several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate. Consequently, a new method of quickly and accurately counting steel rebars with a minimal number of workers needs to be developed to enhance work efficiency and reduce labor costs at construction sites. In this study, the authors developed an automated system to estimate the size and count the number of steel rebars in bale packing using computer vision techniques based on a convolutional neural network (CNN). A dataset containing 622 images of rebars with a total of 186,522 rebar cross sections and 409 poly tags was established for segmentation rebars and poly tags in images. The images were collected in a full HD resolution of 1920×1080 pixels and then center-cropped to 512 × 512 pixels. Moreover, data augmentation was carried out to create 4668 images for the training dataset. Based on the training dataset, YOLACT-based steel bar size estimation and a counting model with a Box and Mask of over 30 mAP was generated to satisfy the aim of this study. The proposed method, which is a CNN model combined with homography, can estimate the size and count the number of steel rebars in an image quickly and accurately, and the developed method can be applied to real construction sites to efficiently manage the stock of steel rebars.

Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework

“…AI-based technology has advantages such as decreasing human error, improving productivity due to repetitive-task fatigue in humans, and predicting risks and taking pre-emptive actions via big data analysis [9,15,34,92]. When a construction organisation is adopting an AI-based technology with these advantages, this study presents considerations that should be involved in the implementation strategy.…”

“…Moreover, the image rectification technique would be an effective method not only to monitor the safety of the construction workers but also to count the number of construction materials [34][35][36]. Son et al [28] proposed a real-time collision warning system for the prevention of heavy equipment and workers using visual data acquired from cameras.…”

The Modification of A* Pathfinding Algorithm for Building Mechanical, Electronic and Plumbing (MEP) Path

“…In addition, when the structure's external and internal MEP route modeling is completed, it will be possible to automate the MEP route design to a level that could suggest a detailed option, such as capacity optimization and the automatic arrangement of accessories based on MEP route model information. Additionally, since building information, including the MEP, has become digitalized, artificial intelligence (AI) can be integrated for a more precise design and the improved optimization of various features of buildings in future research [46][47][48][49]. Further research is required to solve the problems that were encountered in this study, namely, MEP routes following nearwall lines instead of the center of isles, manually placing preferred nodes, and the operation time of searching for a path between main-MEP routes and the sub-MEP equipment.…”

Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning

“…Convolutional neural networks (CNNs) are widely adopted models for classifying objects in images in various fields, such as medical diagnosis, autonomous driving, facial recognition, and so forth [25][26][27][28]. CNNs are applied to various fields in the construction industry, such as structural health monitoring and prediction, health and safety monitoring on a construction site, workplace assessment, and activity recognition of construction workers for predicting hazards [29][30][31][32].…”