HELMET DETECTION OF TWO WHEELED VEHICLE BASED ON YOLOV3-TINY
Aimed at the helmet wearing problem of two-wheeled vehicle drivers and passengers,a lightweighted helmet detection model based on YOLOv3-tiny is proposed.The original model backbone network was light-weighted to reduce the amount of parameters of the detection model,and a U-shaped feature secondary fusion module was added to the network.The DIoU loss function about the distance of bounding boxes was introduced to improve the feature extraction ability of the detection model and recognition accuracy.Experiments on the test set show that the improved model exhibits higher recall rate and mAP and F1-score than the original YOLOv3-tiny model,and it has better detection performance than the deep network YOLOv3 while maintaining a small amount of parameters.
Helmet detectionLightweighted networkFeature fusionBounding box loss function
国家科技期刊平台
NSTL
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