Computer vision-based human-machine collision warning system on construction site

Abstract

With the increase of mechanization in the construction industry, more and more construction machines appear on construction sites. This improves construction efficiency but leads to the high risk of human-machine collision. At present, human-machine collision risks are usually monitored and warned manually. It is difficult to identify relevant risks in time due to the lack of sufficient safety supervisors. This paper proposes an efficient human-machine collision warning system based on computer vision and deep learning. The designed system and relevant devices are installed on construction machinery. It can quickly identify workers and measure the distance between workers and machinery in the complex and dynamic environment so as to quickly warn the risk of human-machine collision based on a threshold. Power supply restriction and harsh environment such as dust and vibration are also considered to ensure the stable and effective operation of the system. The system was tested on a construction site for 6 weeks. The result shows that the system has a significant positive effect on the early warning of human-machine collision risk, thus benefiting the improvement of safety performance of workers in human-machine collaboration scenes.