基于改进YOLOv8的景区行人检测算法
Pedestrian Detection Algorithm for Scenic Spots Based on Improved YOLOv8
贵向泉 1刘世清 1李立 1秦庆松 1李唐艳2
作者信息
- 1. 兰州理工大学计算机与通信学院,甘肃 兰州 730050
- 2. 甘肃省计算中心,甘肃 兰州 730050
- 折叠
摘要
针对当前景区行人检测具有检测精度低、算法参数量大和现有公开数据集在小目标检测上存在限制等问题,创建TAPDataset行人检测数据集,弥补现有数据集在小目标检测方面的不足,并基于YOLOv8算法,构建一种检测精度高、硬件要求低的新模型YOLOv8-L.首先引入DepthSepConv轻量化卷积模块,降低模型的参数量和计算量.其次采用BiFormer注意力机制和上采样算子CARAFE,加强模型对图像的语义理解和信息融合能力,提升模型的检测精度.最后增加一层小目标检测层来提取更多的浅层特征,从而有效地改善模型对小目标的检测性能.在TAPDataset、VOC 2007及TAP+VOC数据集上的实验结果表明,与YOLOv8相比,在FPS基本不变的情况下,在TAPDataset数据集上,模型的参数量减少了18.06%,mAP@0.5提高了5.51%,mAP@0.5∶0.95提高了6.03%;在VOC 2007数据集上,模型的参数量减少了13.6%,mAP@0.5提高了3.96%,mAP@0.5∶0.95提高了6.39%;在TAP+VOC数据集上,模型的参数量减少了14.02%,mAP@0.5提高了4.49%,mAP@0.5∶0.95提高了5.68%.改进算法具有更强的泛化性能,能够更好地适用于景区行人检测任务.
Abstract
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.
关键词
智慧文旅/目标检测/注意力机制/轻量化网络/YOLOv8算法Key words
intelligent cultural tourism/object detection/attention mechanism/lightweight network/YOLOv8 algorithm引用本文复制引用
基金项目
甘肃省重点研发计划-工业类项目(22YF7GA159)
甘肃省基础研究计划-软科学专项(22JR4ZA084)
甘肃省教育厅产业支撑计划项目(2023CYZC-25)
出版年
2024
国家科技期刊平台
NSTL
万方数据