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基于深度学习的车位智能检测方法

Method for Intelligent Detection of Parking Spaces Based on Deep Learning

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摘要

提出了一种基于深度学习的车位智能检测方法。利用TensorFlow深度学习平台对车辆目标识别模型进行了训练, 提取了有效车辆图像的优化间隔, 给出了车辆分布的精准识别结果, 实现了对车辆分布识别结果的有序编号和车位空缺状况的准确判断。利用模拟数据和实际采集数据, 分别验证了车位分布的智能识别、车位智能编号和空车位判断的可靠性。

Abstract

Based on deep learning, one method for the intelligent detection of parking spaces is proposed. The TensorFlow deep learning platform is applied to train the car object recognition model, the optimal interval of the effective car images is extracted, the accurate recognition result of the car distribution is presented, and the order numbering of the recognition results of the car distribution and the accurate judgment of the vacancy situation of parking spaces are realized. The simulation results and the actually collected data are adopted to verify the reliability of intelligent identification of parking space distribution, intelligent numbering of parking space, and the judgement of empty parking space.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.41

DOI:10.3788/cjl201946.0404013

所属栏目:测量与计量

基金项目:重庆市质量技术监督局科研计划(CQZJKY2018004)

收稿日期:2018-12-21

修改稿日期:2019-01-10

网络出版日期:2019-01-22

作者单位    点击查看

徐乐先:武汉理工大学资源与环境工程学院, 湖北 武汉 430079
陈西江:武汉理工大学资源与环境工程学院, 湖北 武汉 430079
班亚:重庆市计量质量检测研究院, 重庆 401120
黄丹:武汉理工大学图书馆, 湖北 武汉 430079

联系人作者:陈西江(cxj_0421@163.com)

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引用该论文

Xu Lexian,Chen Xijiang,Ban Ya,Huang Dan. Method for Intelligent Detection of Parking Spaces Based on Deep Learning[J]. Chinese Journal of Lasers, 2019, 46(4): 0404013

徐乐先,陈西江,班亚,黄丹. 基于深度学习的车位智能检测方法[J]. 中国激光, 2019, 46(4): 0404013

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