Detection of Helmet Wearing of Electric Bicycle Riders Based on Improved YOLOv8s Model
YUAN Yule
TANG Wenbing
Abstract：To address the issues of missed,false detection,low precision,and poor efficiency of real-time detection in electric bicycle helmet detection models caused by factors such as weather and viewing angles,an improved you only look once version 8 small(YOLOv8s)model was proposed based on original YOLOv8s model.The lightweight vanilla network(VanillaNet)module was selected for the backbone feature extraction network,and the lightweight upsampling operator content-aware reassembly of features(CARAFE)module was adopted for the neck network.A 160 pixel×160 pixel tiny object detection layer(tiny)module was added,and the loss function was modified to multi-scale prediction distance intersection over union(MPDIoU).Ablation experiments were conducted to verify the effectiveness of the optimization modules,and the differences before and after model improvement were compared.The results showed that the improved YOLOv8s model achieved a mean average precision of 95.6%,and the detection speed increased to 102 frames/s,significantly improving detection accuracy and reducing latency.The improved YOLOv8s model was able to effectively detect helmet-wearing of electric bicycle riders in real-world scenarios,playing a crucial role in reducing personal injuries,enhancing road safety,and optimizing intelligent transportation systems.
Keywords：deep learningobject detectionYOLOv8sVanillaNetCARAFEtiny object detection layerMPDIoU
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 355-360,367 )