Abstract
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.
Highlights
With the rapid development of national economy, the living standard of Chinese residents has been greatly improved, the travel conditions are more diverse and fast, and the number of private cars is increasing year by year
Due to the relative lag of urban planning and construction, there is a huge gap in the demand for parking space, and the problems of parking and finding a car are becoming increasingly prominent, which increases the efficiency of private car travel
Parking management services need to achieve a variety of functions, mainly including parking information query, map route navigation, display and guidance of spare parking spaces, intelligent payment, and car locating system, involving multidisciplinary technology, including edge computing, image processing technology, smartphone application development technology, and deep learning algorithm [1,2,3]
Summary
With the rapid development of national economy, the living standard of Chinese residents has been greatly improved, the travel conditions are more diverse and fast, and the number of private cars is increasing year by year. Parking Space Recognition Technology Based on Convolution Neural Network Algorithm. Is study will improve the convolutional neural network recognition model and establish a one-time deep learning framework. Integrating YOLO algorithm to improve the real-time performance of convolutional neural network model recognition [15]. Since the objects identified are vehicles in the parking lot and belong to a single class, and the sample size is small, the model integrated with YOLO algorithm can carry out large sample and multiclass complex recognition, which increases the complexity and time of operation [16, 17]. Establishment of Parking Space Recognition System Based on Convolution Neural Network Algorithm. E parking space recognition system based on the improved neural network algorithm is established
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