Detection of human fall behavior based on YOLOv5

To address the issues of slow detection speed and low accuracy in detecting fall behavior among workshop personnel, a deep learning-based fall detection method is proposed. First, the system architecture for detecting fall behavior of workshop personnel is designed. Then, the fall detection algorithm based on YOLOv5 is introduced, including the construction of the network structure and the training of the detection model. Additionally, to facilitate viewing the detection results, a visualization interface for the results is designed. Finally, system testing is conducted through experiments. The results show that, compared to several other common object detection algorithms, the YOLOv5-based fall detection method offers advantages such as fast speed, high accuracy, low cost, and good stability. It can be applied to monitor fall behavior in workshops, helping to reduce safety accidents during production processes.