Improved YOLOv5 Network Model and Application in Safety Helmet Detection

Due to the complex background of the building site and the diverse sorts of construction personnel, relying on traditional manual inspections and video surveillance methods to detect the wearing of personnel helmets has poor timeliness and missed inspections. This article provides a method based on deep learning to resolve the above issues. First, improvement based on YOLOv5, added a functionality detection scale to allow it to get smaller targets; second, by introducing the DloU-NMS instead of NMS, DIoU also considers the overlap area and the center distance of the two boxes, making it more accurate in suppressing the predicted bounding box. The experimental results show that the proposed algorithm significantly improves the accuracy compared to the YOLOv5 network model, and detection speed is 98 frames per second, which can meet the needs of real-time detection.