Automated Counting of Steel Construction Materials: Model, Methodology, and Online Deployment

Abstract

Construction material management is crucial for promoting intelligent construction methods. At present, the manual inventory of materials is inefficient and expensive. Therefore, an intelligent counting method for steel materials was developed in this study using the object detection algorithm. First, a large-scale image dataset consisting of rebars, circular steel pipes, square steel tubes, and I-beams on construction sites was collected and constructed to promote the development of intelligent counting methods. A vision-based and accurate counting model for steel materials was subsequently established by improving the YOLOv4 detector in terms of its network structure, loss function, and training strategy. The proposed model provides a maximum average precision of 91.41% and a mean absolute error of 4.07 in counting square steel tubes. Finally, a mobile application and a WeChat mini-program were developed using the proposed model to allow users to accurately count materials in real time by taking photos and uploading them. Since being released, this application has attracted more than 28,000 registered users.