This research designs an intelligent parking system including service application layer, perception layer, data analysis layer, and management layer. The network system adopts opm15 system, and the parking space recognition adopts improved convolution neural networks (CNNs) algorithm and image recognition technology. Firstly, the parking space is occupied and located, and the shortest path (Dynamic Programming, DP) is selected. In order to describe the path algorithm, the parking system model is established. Aiming at the problems of DP low power and adjacent path interference in the path detection system, a method of combining interference elimination technology with enhanced detector technology is proposed to effectively eliminate the interference path signal and improve the performance of the intelligent parking system. In order to verify whether the CNNs system designed in this study has advantages, the simulation experiments of CNNs, ZigBee, and manual parking are carried out. The results show that the parking system designed in this study can control the parking error, has smaller parking error than ZigBee, and has more than 25.64% less parking time than ZigBee, and more than 34.83% less time than manual parking. In terms of parking energy consumption, when there are less free parking spaces, CNNs have lower energy consumption.

中文翻译：

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基于改进神经网络算法和智能图像分析的自动停车系统

本研究设计了一个智能停车系统，包括服务应用层、感知层、数据分析层和管理层。网络系统采用opm15系统，车位识别采用改进的卷积神经网络（CNNs）算法和图像识别技术。首先，停车位的占用和定位，选择最短路径（动态规划，DP）。为了描述路径算法，建立了停车系统模型。针对路径检测系统中DP低功率和相邻路径干扰的问题，提出一种干扰消除技术与增强检测器技术相结合的方法，有效消除干扰路径信号，提高智能停车系统的性能。为了验证本研究设计的CNNs系统是否具有优势，进行了CNNs、ZigBee、人工停车的仿真实验。结果表明，本研究设计的停车系统能够控制停车误差，停车误差比ZigBee更小，停车时间比ZigBee减少25.64%以上，比人工停车减少34.83%以上。在停车能耗方面，当免费停车位较少时，CNN 的能耗较低。比人工停车时间减少34.83%以上。在停车能耗方面，当免费停车位较少时，CNN 的能耗较低。比人工停车时间减少34.83%以上。在停车能耗方面，当免费停车位较少时，CNN 的能耗较低。