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

Article Sidebar

Main Article Content

Ruchika Wadbudhe
Vaishnavi Ganesh
Achal Patil
Aditya Meshram
Aman Sayyed
Aniket Hedau
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.
Uni Find: Lost and Found Objects Management System for Campus using AI & Cloud Services. (2025). International Journal of Latest Technology in Engineering Management & Applied Science, 14(10), 134-139. https://doi.org/10.51583/IJLTEMAS.2025.1410000017

Downloads

References

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.

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.

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.

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:

1016/j.autcon.2023.105103.

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.

Y. Wu, "Fusion-based modeling of an intelligent algorithm for enhanced object detection using a Deep Learning

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

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.

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.

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.

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.

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.

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.

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.

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.

Y. Zhang, C. Yip, E. Lu, and Z. Y. Dong, “A Systematic Review on Technologies and Applications in Smart Campus:

A Human-Centered Case Study,” IEEE Access, vol. 10, pp. 16134-16149, Feb. 2022, doi:

1109/ACCESS.2022.3148735.

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.

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.

Article Details

How to Cite

Uni Find: Lost and Found Objects Management System for Campus using AI & Cloud Services. (2025). International Journal of Latest Technology in Engineering Management & Applied Science, 14(10), 134-139. https://doi.org/10.51583/IJLTEMAS.2025.1410000017