Computer Vision Technology in Cost Monitoring of Construction Projects

Abstract

With the continuous development of technology, the application of computer vision technology in cost monitoring of construction projects is becoming increasingly important. By utilizing computer vision technology, construction companies can monitor the progress of construction sites, material usage, and allocation of human resources in real-time, thereby better controlling project costs. The application of this technology can not only improve the efficiency of construction projects, but also reduce human errors and waste, which is of great significance for the successful completion of projects. Therefore, construction companies should actively adopt computer vision technology to improve the efficiency and accuracy of cost monitoring. In recent years, China's economy has continued to grow, continuously driving the development of infrastructure and manufacturing industries. However, at the same time, the production safety situation in China is becoming increasingly severe, and safety accidents continue to occur. One of the main causes of safety accidents is human misconduct, which includes workers wearing uniforms and safety helmets improperly during construction. Workers engage in some dangerous behaviors during construction, such as making phone calls, falling, and squatting for long periods of time. In response to the above issues, this article studies how the SSD (Single Shot MultiBox Detector) algorithm, CNN (Convolutional Neural Networks) algorithm, and YOLO (You only look once) algorithm can be applied to the behavior detection of construction workers, and compares them with existing object detection algorithms through experiments. The experimental data shows that the AP (Average precision), AP50, and AP75 values of the SSD-CNN algorithm in this article are 51.29%, 69.85%, and 54.81%, respectively. Among all the algorithms, the values of the three indicators rank in the top few.