Pedestrian Detection Algorithm for Scenic Spots Based on Improved YOLOv8
The TAPDataset pedestrian detection dataset is used in this study to address the issues of low detection accuracy,large number of algorithm parameters,and limitations of existing public datasets for small target detection in current scenic pedestrian detection.This dataset addresses the deficiencies of existing datasets regarding small target detection.Based on the YOLOv8 algorithm,a new model with high detection accuracy and low hardware requirements,called YOLOv8-L,is proposed.First,the lightweight convolution module DepthSepConv is introduced to reduce the number of parameters and computations of the model.Second,the BiFormer attention mechanism and CARAFE upsampling operator are used to enhance the model's semantic understanding of images and information fusion capability,significantly improving detection accuracy.Finally,a small target detection layer is added to extract more shallow features,effectively improving the model's performance for small target detection.The effectiveness of the algorithm is verified using the TAPDataset,VOC 2007,and TAP+VOC datasets.The experimental results show that compared with YOLOv8,the number of model parameters is reduced by 18.06%on the TAPDataset with unchanged FPS,mAP@0.5 improves by 5.51%,and mAP@0.5∶0.95 improves by 6.03%.On the VOC 2007 dataset,the number of parameters is reduced by 13.6%,with mAP@0.5 improving by 3.96%and mAP@0.5∶0.95 improving by 6.39%.On the TAP+VOC dataset,the number of parameters is reduced by 14.02%,with mAP@0.5 improving by 4.49%and mAP@0.5∶0.95 improving by 5.68%.The improved algorithm demonstrates stronger generalization performance and can be better applied to scenic pedestrian detection tasks.
intelligent cultural tourismobject detectionattention mechanismlightweight networkYOLOv8 algorithm
国家科技期刊平台
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