An IoT based Intelligent Parking System for the Unutilized Parking Area with Real-Time Monitoring using Mobile and Web Application

Kabir, A. Z. M. Tahmidul; Mizan, Al Mamun; Saha, Plabon Kumar; Hasan, Md. Shajedul; Pramanik, Mohitosh; Ta-sin, Akib Jawad; Johura, Fatema Tuj; Hossain, Akil Mohammad

doi:10.1109/conit51480.2021.9498286

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…Phase of exploitation (no object collisions) 4.2. If the collision threshold is greater than 0.5, then the acceleration of each object is updated for the next iteration at time t+1 using (9).…”

Section: Phase Of Exploration (In Which Items Collide)mentioning

confidence: 99%

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AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

et al. 2024

Int. Res. J. multidiscip. Technovation

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Uneven illumination has a significant impact on vision-based automatic parking systems, making it impossible to conduct a correct assessment of parking places in the presence of complicated picture data. In to address this issue, this work provides a deep learning-based system for visual recognition of parking spaces and picture processing. Artificial intelligence (AI) approaches can be used to identify a less expensive and easier-to-implement solution to the parking spot identification challenge, especially since the discipline of deep learning is reshaping the world. Using deep learning techniques, this study offers a dynamic, straightforward, and cost-effective algorithm for the detection of parking spots. In order to determine which parking spots are available and which are occupied, this method employs a Masked Region Based Convolutional Neural Network (MR-CNN) and the intersection over union approach. Cars in the training dataset were spaced more apart than those actually seen, which increased the accuracy of the identification between cars and parking spots. The AOA mechanism enhances the model's ability to focus on relevant regions within an image, improving accuracy in detecting parking spaces. This leads to precise identification of parking slots, reducing false positives and negatives. The sequence and quantity of parking spots, as well as the capacity to predict empty spots, were tested in a case study and found to be accurate. In the experimental results as the AOA based MR-CNN model stretched the accuracy as 98.50 and the recall value as 40.59 then the precision as 96.34 F1-measure as 57.95 correspondingly.

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Section: Phase Of Exploration (In Which Items Collide)mentioning

confidence: 99%

“…Recent years have been a focus on visual detection in AI studies, especially as they relate to autonomous vehicles. In conventional parking, when distance is detected via radar, a reference car must be present on either side of the parking place [9]. In addition, there are quite specific rules about where and how you can park.…”

Section: Introductionmentioning

confidence: 99%

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

et al. 2024

Int. Res. J. multidiscip. Technovation

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“…Users will have to have a mobile device and install the application (which will be available for both Android and IOS) to be able to use the system. Having a mobile device is crucial to reserving a spot, entering, and exiting the gates, and automatically performing a successful payment [21].…”

Section: Mobile Devicementioning

confidence: 99%

IOT Based Smart Parking System Using Ensemble Learning

Elashmawi¹,

Ahmad²,

Yasser³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Parking space is usually very limited in major cities, especially Cairo, leading to traffic congestion, air pollution, and driver frustration. Existing car parking systems tend to tackle parking issues in a non-digitized manner. These systems require the drivers to search for an empty parking space with no guarantee of finding any wasting time, resources, and causing unnecessary congestion. To address these issues, this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of parking availability. User authentication and automated payments are handled using a quick response (QR) code on entry and exit. Some experiments were done on real data collected for six different locations in Cairo via a live popular times library. Several machine learning models were investigated in order to estimate the occupancy rate of certain places. Moreover, a clear analysis of the differences in performance is illustrated with the final model deployed being XGboost. It has achieved the most efficient results with a R 2 score of 85.7%.

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“…The application adds Google Maps API in the application and shows the breadcrumbs. A similar approach was taken by other previous projects to achieve this task [41] - [43]. To see parking details such as available parking spot, hourly rate as per vehicle type, specific address by clicking on each breadcrumb.…”

Section: F Online Parking With Mobile Applicationmentioning

confidence: 99%

An IoT Based Intelligent Parking System for the Unutilized Parking Area With Real-Time Monitoring Using Mobile and Web Application

Kabir¹,

Mizan²,

Saha³

et al. 2021

AJCT

Self Cite

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The parking place number still remains insufficient despite the continuous growth in vehicle number. This study proposed a solution using modern technology to resolve these parking issues. The proposed solution is an automated parking management system that reduces parking scarcity by providing unused car parking places. This project has used IoT, mobile application, a web application to serve the following services, parking management, parking place availability monitorization, real-time invoice generation, payment system and, parking management. By proving this system, the hassle of people will be reduced as they are getting updates on nearby available parking spots. Alongside IoT, mobile application, web application other technologies such as GPS -GSM module, image processing method, and other technologies were integrated. The system offers both web and android app-based solutions for better customer experience and easy usage. Moreover, there is also an offline mode to use the system.

show abstract

An IoT based Intelligent Parking System for the Unutilized Parking Area with Real-Time Monitoring using Mobile and Web Application

Cited by 12 publications

References 27 publications

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

IOT Based Smart Parking System Using Ensemble Learning

An IoT Based Intelligent Parking System for the Unutilized Parking Area With Real-Time Monitoring Using Mobile and Web Application

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