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面向站口行人检测的改进型Yolov5s算法OA北大核心CSTPCD

Improved Yolov5s algorithm for pedestrian detection at station entrances

中文摘要英文摘要

针对现有站口行人检测方法难以在实时性与准确性之间均衡的问题,提出一种改进型的Yolov5s模型用于高效地检测站口行人.首先,基于EfficientNetV1改进提出轻量化主干网络EfficientNet_c,优化网络结构和基本单元堆叠次数,提高模型在浅层对小尺寸目标的特征提取能力和提取速度;其次,通过调整宽度因子为基础模型的1/2,改变模型特征层通道数,在较小的精度损失情况下降低模型参数量;再次,增加小目标检测层,优化模型特征提取能力,提高模型对小目标的敏感度和准确性;最后,利用迁移学习的方式优化模型,增强模型泛化能力,降低学习成本,进一步提升模型精度.在课题组收集的数据集上的实验结果表明,所提算法准确率为92.2%,模型参数量仅为1.4M.在Tesla P100 GPU上的平均推理速度为7.7 ms,实现模型准确率和推理速度的提升.研究结果为地铁和火车站口的行人检测和流量统计提供了一种可行的解决方案.

Aiming at the problem that existing pedestrian detection method is difficult to strike a balance between real-time performance and accuracy,an improved Yolov5s model is proposed for efficient pedestrian detection at station entrances.First,the lightweight main network Efficientnet_c is improved based on the improved EfficientNetV1,and the network structure and stacking times of basic units are optimized to enhance the feature extraction capability and speed of the model for small targets at the shallow layer.Secondly,by adjusting the width factor as 1/2 of the basic model,the channel number of feature layer of the model is changed,and the number of model parameters is reduced in the case of small precision loss.Thirdly,a small target detection layer is added to optimize the feature extraction ability of the model and improve the sensitivity and accuracy of the model to small targets.Finally,transfer learning is used to optimize the model,enhance the generalization ability of the model,reduce the learning cost,and further improve the accuracy of the model.The experimental results on the data set collected by the research group show that the accuracy of the proposed algorithm is 92.2%,and the number of model parameters is only 1.4 M.The average inference speed on Tesla P100 GPU is 7.7 ms,which realizes the improvement of model accuracy and inference speed.The results provide a feasible solution for pedestrian detection and traffic statistics of subway and railway station.

李林红;杨杰;冯志成;朱浩

江西理工大学电气工程与自动化学院,赣州,341000||江西省磁悬浮技术重点实验室,赣州,341000江西理工大学电气工程与自动化学院,赣州,341000

计算机与自动化

站口行人检测Yolov5sEfficientNet_c宽度因子小目标检测层迁移学习

pedestrian detection at station entrancesYolov5sEfficientNet_cwidth factorsmall object detection layertransfer learning

《南京大学学报(自然科学版)》 2024 (001)

87-96 / 10

国家自然科学基金(62063009)

10.13232/j.cnki.jnju.2024.01.009

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