Automatic Parking Space Detection System

Author(s):  
Nazia Bibi ◽  
Muhammad Nadeem Majid ◽  
Hassan Dawood ◽  
Ping Guo
2015 ◽  
Vol 2537 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Khaled Shaaban ◽  
Houweida Tounsi

This study proposes a novel method for parking space detection. The proposed system is based on individual vehicle detection using grayscale images acquired from a video camera. Two algorithms were tested in the laboratory and the field. The first algorithm was based on the maximum value of the image histogram; the second algorithm was based on the bandwidth of the image histogram. The proposed algorithms successfully recognized vacant and occupied parking spaces under different scenarios and weather conditions. From the verification of the field study, the detection rate of the proposed system reached more than 98% for both algorithms. This system can be used for monitoring parking vacancy and guiding incoming motorists to vacant parking spaces in real time. The system has simple algorithms and easy configuration and does not require high-quality images. The latter feature means that less expensive cameras or existing surveillance cameras can easily be used instead of special cameras; thus huge cost savings are provided. The system also offers a fast processing time and easy applicability to parking lots in continuous operation.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yi Xu ◽  
Shanshang Gao ◽  
Guoxin Jiang ◽  
Xiaotong Gong ◽  
Hongxue Li ◽  
...  

The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yucheng Guo ◽  
Hongtao Shi

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.


2021 ◽  
Vol 11 (04) ◽  
pp. 688-701
Author(s):  
Diana Laura Gómez-Ruíz ◽  
Daphne Espejel-García ◽  
Graciela Ramírez-Alonso ◽  
Vanessa Verónica Espejel-García ◽  
Alejandro Villalobos-Aragón

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