Authors:
Jacob Azaare, Liu Mei-Ling, Fung Nga-Ki, Mohd Abdullah Al Mamun, Wu Chien-Kuo, Jamila Shaaban Ebrahim
Addresses:
Department of Business Computing, School of Computing and Information Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Upper East Region, Ghana. Department of Information Technology, Sunrise Tech Solutions, Taoyuan, Taiwan. Department of Information Technology, Bright Star Solutions, Wan Chai, Hong Kong. Department of Information Technology Management, Westcliff University, Irvine, California, United States of America. Department of Manufacturing, Golden Isle Manufacturing, Daliao District, Kaohsiung, Taiwan. Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Aleppo University, Aleppo, Syria.
Abstract:
The problem of parking in cities has been exacerbated by the rapid increase in the number of vehicles, leading to traffic jams, inefficient use of available parking spots, increased fuel consumption, and driver frustration. To address these issues, this paper presents a cost-effective and intelligent parking-space monitoring system based on image-processing and deep-learning techniques. The suggested system monitors parking lots using strategically placed cameras to assess parking space occupancy in real time automatically. The technology employs a deep-learning Mask R-CNN model to recognise and segment automobiles in collected photos to better distinguish occupied from unoccupied parking spaces. The system displays parking availability by processing and updating parking information in real time. Cloud integration enables the system to save, manage, and view parking data remotely. It may be deployed to several parking lots without major infrastructure changes, allowing flexibility and scalability. The technique helps urban areas with limited parking and economic needs. The device recognises parking spaces in real time and streamlines payment with QR codes. Scan the QR code for secure, contactless parking payments. It reduces parking wait times and cash payments. Smart-parking solutions using image analysis, cloud technology, and QR-based payment improve parking management, operational efficiency, and user convenience while optimising precious urban parking resources. To conclude, the proposed system delivers practical, scalable, intelligent, user-friendly smart parking facilities.
Keywords: Driver Frustration; Cloud Storage Integration; Parking Management; Intelligent Parking Space; Fuel Consumption; Cloud Technology; Operational Efficiency.
Received on: 13/09/2025, Revised on: 02/11/2025, Accepted on: 13/11/2025, Published on: 07/06/2026
DOI: 10.64091/ATICR.2026.000343
AVE Trends in Intelligent Computing Research, 2026 Vol. 1 No. 2 , Pages: 114-124