Uni Find: Lost and Found Objects Management System for Campus using AI & Cloud Services

Abstract—The rapid expansion of university campuses has led to frequent instances of misplaced personal belongings, creating inconvenience for students and staff. UniFind: Lost and Found Objects Management System for Campus using AI & Cloud Services is designed to provide a smart, efficient, and scalable solution to this challenge. The system leverages artificial intelligence for image recognition and natural language processing to accurately identify, classify, and match lost items with their rightful owners. By integrating with cloud services, UniFind ensures secure data storage, real-time accessibility, and seamless scalability for large user bases. The platform enables users to report lost or found items via a web or mobile interface, where AI-driven matching algorithms automatically suggest potential matches. Notifications and dashboards streamline communication between finders and owners, significantly reducing manual effort and delays. This project demonstrates how the fusion of AI and cloud technology can modernize campus management processes, enhance user convenience, and promote a responsible, collaborative environment within academic institutions.

Lost and Found System, Campus Management, Artificial Intelligence (AI), Image Recognition, Natural Language Processing (NLP), Cloud Computing, Object Classification, Smart Search and Matching, Real-time Notifications, Web and Mobile Application,, Scalable Architecture, Data Security, Automation in Campus Services

1. S. Abba, A. M. Bizi, J.-A. Leeb, S. Bakouri, and M. L. Crespo, "Real-time object detection, tracking, and monitoring framework for security surveillance systems," Heliyon, vol. 10, Apr. 2024, Art. no. e34922, doi: 10.1016/j.heliyon.2024.e34922. [Google Scholar] [Crossref]

2. C.-W. Chou and Y.-T. Hsu, "Robust real-time object detection and counting system for casting foundries," Appl. Soft Comput. J., vol. 176, Apr. 2025, Art. no. 113155, doi: 10.1016/j.asoc.2025.113155. [Google Scholar] [Crossref]

3. S. Jin, Z. Cao, and C. Yu, "Two-stage vision system: Application of multi-perspective object detection network and character recognition network in industrial product classification," Eng. Appl. Artif. Intell., vol. 156, May 2025, Art. no. 111190, doi: 10.1016/j.engappai.2025.111190. [Google Scholar] [Crossref]

4. S. Kim, S. H. Hong, H. Kim, M. Lee, and S. Hwang, "Small object detection (SOD) system for comprehensive construction site safety monitoring," Autom. Constr., vol. 156, Oct. 2023, Art. no. 105103, doi: [Google Scholar] [Crossref]

5. 1016/j.autcon.2023.105103. [Google Scholar] [Crossref]

6. A. Kos, K. Majek, and D. Belter, "Enhanced lightweight detection of small and tiny objects in high- resolution images using object tracking-based region of interest proposal," Eng. Appl. Artif. Intell., vol. 153, Apr. 2025, Art. no. 110852, doi: 10.1016/j.engappai.2025.110852. [Google Scholar] [Crossref]

7. Y. Wu, "Fusion-based modeling of an intelligent algorithm for enhanced object detection using a Deep Learning [Google Scholar] [Crossref]

8. Approach on radar and camera data," Inf. Fusion, vol. 113, Aug. 2024, Art. no. 102647, doi: 10.1016/j.inffus.2024.102647. [Google Scholar] [Crossref]

9. S. B. J. Khan, C. Li, and P. Zhang, "FocusTrack: Enhancing object detection and tracking for small and ambiguous objects," J. Vis. Commun. Image R., vol. 111, Aug. 2025, Art. no. 104549, doi: 10.1016/j.jvcir.2025.104549. [Google Scholar] [Crossref]

10. M. Nikouei et al., "Small object detection: A comprehensive survey on challenges, techniques and real- world applications," Intell. Syst. Appl., vol. 27, Jul. 2025, Art. no. 200561, doi: 10.1016/j.iswa.2025.200561. [Google Scholar] [Crossref]

11. S. Fu, Q. Zhao, H. Liu, Q. Tao, and D. Liu, "Low- light object detection via adaptive enhancement and dynamic feature fusion," Alexandria Eng. J., vol. 126, Apr. 2025, pp. 60–69, doi: 10.1016/j.aej.2025.04.047. [Google Scholar] [Crossref]

12. Iqra and K. J. Giri, "SO-YOLOv8: A novel deep learning-based approach for small object detection with YOLO beyond COCO," Expert Syst. Appl., vol. 280, Apr. 2025, Art. no. 127447, doi: 10.1016/j.eswa.2025.127447. [Google Scholar] [Crossref]

13. D. Nimma et al., "Object detection in real-time video surveillance using attention based transformer- YOLOv8 model," Alexandria Eng. J., vol. 118, Jan. 2025, pp. 482–495, doi: 10.1016/j.aej.2025.01.032. [Google Scholar] [Crossref]

14. J. W. Ma, T. Czerniawski, and F. Leite, “An application of metadata-based image retrieval system for facility management,” Future Generation Computer Systems, vol. 8, pp. 265–288, Mar. 2020, doi: 10.1016/j.future.2017.11.015. [Google Scholar] [Crossref]

15. P. Choudhary, A. Singh, A. K. Choudhary, and A. P. Srivastava, “Find Mine: Find the Lost Items via Mobile App,” in 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 2021, pp. 491-495, doi: 10.1109/ICIEM51511.2021.9445379. [Google Scholar] [Crossref]

16. J. Yang, B. Jiang, and H. Song, “A distributed image-retrieval method in multi-camera system of smart city based on cloud computing,” Future Generation Computer Systems, vol. 84, pp. 11– 26, Jul. 2018, doi: 10.1016/j.future.2017.11.015. [Google Scholar] [Crossref]

17. Y. Zhang, C. Yip, E. Lu, and Z. Y. Dong, “A Systematic Review on Technologies and Applications in Smart Campus: [Google Scholar] [Crossref]

18. A Human-Centered Case Study,” IEEE Access, vol. 10, pp. 16134-16149, Feb. 2022, doi: [Google Scholar] [Crossref]

19. 1109/ACCESS.2022.3148735. [Google Scholar] [Crossref]

20. B. George and O. Wooden, “Managing the Strategic Transformation of Higher Education through Artificial Intelligence,” Administrative Sciences, vol. 13, no. 9, p. 196, Aug. 2023, doi: 10.3390/admsci13090196. [Google Scholar] [Crossref]

21. S. H. Gill et al., “Security and Privacy Aspects of Cloud Computing: A Smart Campus Case Study,” Intelligent Automation & Soft Computing, vol. 31, no. 1, pp. 117-128, Feb. 2022, doi: 10.32604/iasc.2022.016597. [Google Scholar] [Crossref]

• Decision Tree and Automatic Linear Modeling Approaches to Predict Body Weight in Indigenous Sabi Sheep and Matebele Goat Females of Zimbabwe
• Microcontroller – Based Automatic Railway Crossing Control and Track Obstacle Monitoring System
• Modelling the Role of Absorptive Capacity in Foreign Direct Investment - Economic Growth Nexus: A Focus on South Africa’s Manufacturing Sector
• AI-Powered Wristband for Accurate BAC Monitoring Using Smart Data Fusion
• Climate Change Impacts on Different Regions