基于深度学习的行人检测方法在图书馆中的应用研究

Application of Deep Learning Based on Pedestrian Detection in Library

深度学习和计算机视觉技术在产业升级中发挥了重要的价值,是建设下一代智慧图书馆的关键技术,而行人检测技术是深度学习结合计算机视觉技术重要的研究应用方向。本文探索了行人检测技术在图书馆中的应用,探讨了该技术对图书馆服务的重要意义,并且成功地将此项技术的代表方法YOLOv3实践在西北工业大学图书馆场景中,详细讨论了行人检测技术如何提高西北工业大学图书馆服务的应用实例。我们在行人数据库上训练了YOLOv3网络模型,并将其部署在西北工业大学的图书馆中,代表性场景的行人检测结果表明,YOLOv3在精度和实时性上能够满足图书馆场景下行人检测的需求。本文是将深度学习和计算机视觉技术用于构建智慧图书馆的一次有益尝试,也为其他相关技术在图书馆的落地实践提供了较好的借鉴作用。

Deep learning and computer vision play an important role and is of great value in industrial upgrading;they are key technologies for constructing next-generation intelligent library.Pedestrian detection is an important research and application direction of this field.This work explores the application of pedestrian detection method to library for the first time,and discusses the important value of the related applications in detail based on some real examples.The authors create the pedestrian detection representative method-YOLOv3 on open pedestrian databases,and successfully deploy the detection system in NWPU library.Some representative scenarios show that YOLOv3 can satisfy library needs both in terms of accuracy and of run-time performance.This work is a valuable attempt of applying deep learning and computer vision technology in smart library field,and has good reference for utilizing related technologies in library.