Analysis of Helmet Wearing Object Detection Technology Based on Jetson,Nano,and Improved YOLOv7 Algorithm
This paper describes that the YOLOv7 tiny algorithm has low detection accuracy on the target detection dataset for helmet wearing,and is prone to false positives and missed detections under poor lighting conditions,blurred background,and obstructed targets.To this end,a targeted CBAM attention mechanism is added to improve the model.The feature maps are first input into the channel attention module to enhance the model's attention to the target object,and then the spatial attention mechanism is used to enable the model to better learn the position information of the target.By improving the network structure of the YOLOv7 tiny algorithm,a 91％accuracy was achieved,which was 0.5％higher than the YOLOv7 tiny(90.5％)accuracy.After simulation testing,it was found that the improved model can effectively reduce the probability of false positives and missed detections,and can achieve high-precision,low misjudgment deployment on low computing power platforms,thereby effectively achieving supervision of helmet wearing.
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