Research on improved helmet wear detection algorithm based on YOLOv8
Wearing a safety helmet is crucial for construction site and factory workers.Wearing a safety helmet is an important protective measure at the construction site.How to effectively monitor and ensure the wearing of safety helmets has always been a major challenge for corporate supervision.First,this paper improves the detection accuracy based on the YOLO v8 algorithm by replacing the backbone network and head network of the YOLOv8 network.Experimental results show that the detection accuracy of the improved algorithm reaches 74.4%,Compared to YOLOv8,there has been an improvement of 0.4 percentage points.This improvement is used to improve the accuracy of helmet detection and is of great significance meaning.
Deep LearningTarget DetectionHelmet DetectionYOLOv8
NETL
NSTL
万方数据
