基于深度学习的救护车辆识别系统设计  

Design of an Ambulance Vehicle Identification System Based on Deep Learning

作　　者：张丽林 Zhang Lilin(School of Information Engineering,Jiangxi Polytechnic University,Jiujiang,Jiangxi,332005)

机构地区：[1]江西职业技术大学信息工程学院,江西九江332005

出　　处：《九江职业技术学院学报》2025年第1期38-44,共7页Journal of Jiujiang Vocational and Technical College

基　　金：江西省教育厅科学技术研究项目课题“基于深度学习算法的救护车辆识别与跟踪模型研究”(项目编号:GJJ2204824)。

摘　　要：本文提出并实现了一种基于深度学习的救护车辆识别系统。系统利用百度云深度学习平台及预训练的图像分类模型,通过API调用实现救护车辆的自动检测。前端采用Vue.js框架与Element UI组件库开发,后端基于Spring Boot框架,并与MySQL数据库结合,实现识别结果的高效存储与检索。本设计优化了前后端数据交互流程和数据库存储机制,确保了系统的实时性与稳定性。测试结果表明,系统在多种复杂环境下的平均识别准确率达90.99%,部分图像的最高识别置信度为99.99%。限于网络速度,单次识别的平均响应时间为1.40秒。未来将进一步优化模型性能、扩展数据集规模,并提升系统在不同网络环境下的实时性,以更好地支持智慧城市的建设.This paper proposes and implements a deep learning-based ambulance recognition system.The system utilizes Baidu Cloud's deep learning platform and a pre-trained image classification model to automate ambulance detection through API calls.The front end is developed using the Vue.js framework and the Element UI component library,while the back end is based on the Spring Boot framework and integrates with a MySQL database for efficient storage and retrieval of recognition results.This paper optimizes the data interaction process between the front and back ends as well as the database storage mechanism,ensuring real-time performance and stability.Test results indicate that the system achieves an average recognition accuracy of 90.99%under various complex conditions,with a maximum confidence of 99.99%for some images.Due to network conditions,the average response time for a single recognition is 1.40 seconds.In the future,the model performance will be further optimized,the dataset size will be expanded,and real-time performance in different network environments will be improved to better support the development of smart cities.

关 键 词：救护车辆识别 深度学习 智能交通 人工智能 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U495[自动化与计算机技术—计算机科学与技术]