Automated Vehicle Parking Slot Detection System Using Deep Learning

Sairam, Bandi; Agrawal, Aditi; Krishna, Gopi; Sahu, Satya Prakash

doi:10.1109/iccmc48092.2020.iccmc-000140

Cited by 17 publications

(5 citation statements)

References 13 publications

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“…Finally, the connected regions extracted were determined as parking spaces by the geometric relationship of the four vertices. Experimental results show that the proposed method can accurately detect and recognize parking spaces in the case of uneven illumination or complex background, which complemented the work after parking space detection in the paper by Bandi et al [13].…”

Section: Tpsupporting

confidence: 62%

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Visual Detection and Image Processing of Parking Space Based on Deep Learning

Huang

Yang

Luo

et al. 2022

Sensors

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The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces.

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Section: Tpsupporting

confidence: 62%

“…Yu et al [12] proposed a detector using a Convolutional Neural Network (CNN) to obtain faster speed and smaller model size while maintaining the detection accuracy of parking space. Bandi et al [13] used multiple surveillance cameras to obtain real-time information. The parking spaces were detected and classified by the Mask R-CNN model.…”

Section: Introductionmentioning

confidence: 99%

Visual Detection and Image Processing of Parking Space Based on Deep Learning

Huang

Yang

Luo

et al. 2022

Sensors

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“…In [1] an automated vehicle parking slot detection system utilizes Mask R CNN and PSDL for efficient instance segmentation, marking point detection, and free space identification between vehicles. The approach focuses on vision-based car parking technology using web cameras and addresses infrastructure maintenance and cost concerns associated with sensor-based systems.…”

Section: Literature Surveymentioning

confidence: 99%

Car Parking Slot Detection Using Yolo Algorithm

2024

IJSREM

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Finding free slots in the parking area has become a major problem that the modern society is facing nowadays, which also leads to traffic overcrowding at the parking area. As the number of vehicles are increasing at a quick pace find parking slots has become difficult. The proposed paper helps to solve the parking problem. In traditional parking system a person has to find vacant slot or a gate keeper needs to inform about the available parking slot at parking area. To address the issue, a system where the user will be able to know the number of vacant slots in a particular parking area along with total number of slots at the entrance of the parking area. As a critical component of this project, by built a car parking slot detection system which collects data from a camera at parking area. Today's most popular object detection model YOLO, is trained to achieve desired results with good precision scores at real-time speed. The model will be getting all the parking slots that are present in the parking area, to which marking boxes are drawn around for training. Real time processing is performed on the obtained data by the model to find whether the slot is vacant or occupied. Thus, it gives the information of available vacant slots to park the car at parking area. Key Words: YOLO, Object detection, Computer vision, Machine learning.

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“…However, no details about these features nor results are given (thus, this work is not considered in Sections 5.2 and 5.3). For the individual parking spaces classification, the authors report quantitative results only for the training and validation phases in the PKLot dataset (results for the test phase are given in a plot according to the training epoch, making its analysis difficult).DL models for detection and segmentation were employed inMartín Nieto et al (2019) andSairam et al (2020) to aid vehicle detection Martín Nieto et al (2019). used Faster R-CNN(Ren et al, 2015) and multiple cameras from their proposed PLds dataset.…”

mentioning

confidence: 99%

“…Thereafter, the cars detected by the different cameras were fused to classify the parking spots. The Faster R-CNN was also used inKhan et al (2019), where the authors focused on tests involving different camera angles and parking lot changes using the PKLot dataset.More recently,Sairam et al (2020) proposed a method based on the Mask R-CNN(He et al, 2017) network. It was used to extract individual vehicles and to detect the proportion of the parking space the vehicles are occupying to differentiate between cars and two-wheel vehicles.…”

mentioning

confidence: 99%

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

de Almeida,

Alves,

Parpinelli

et al. 2022

Preprint

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Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently present a systematic and comprehensive review of existing works that employ such datasets. The literature review identified relevant gaps that require further research, such as the requirement of dataset-independent approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have noticed that several important factors such as the presence of the same cars across consecutive images, have been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis of the datasets also revealed that certain features that should be present when developing new benchmarks, such as the availability of video sequences and images taken in more diverse conditions, including nighttime and snow, have not been incorporated.

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Automated Vehicle Parking Slot Detection System Using Deep Learning

Cited by 17 publications

References 13 publications

Visual Detection and Image Processing of Parking Space Based on Deep Learning

Visual Detection and Image Processing of Parking Space Based on Deep Learning

Car Parking Slot Detection Using Yolo Algorithm

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

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