V3I3P17

SEE YOU IN CEU: A Digital Lost and Found System for Campus Using Convolutional Neural Network (CNN)

Julianna Marie M. Quitlong¹, Domnic Gabriel E. Escalante²,  Joemel L. Bernardino³*, Eliza B. Ayo⁴

Abstract

Traditional lost-and-found systems in university campuses are largely manual, fragmented, and susceptible to administrative inefficiencies. This study presents the design, development, and evaluation of See You in CEU, a mobile-based digital lost-and-found system developed for Centro Escolar University (CEU) Manila. The system leverages a Convolutional Neural Network (CNN)-based image recognition module integrated through TensorFlow Lite to automatically extract descriptive keywords from uploaded images of lost items. Rather than relying on computationally intensive matching algorithms, the platform utilizes a lightweight keyword-based matching mechanism to ensure cost-efficiency and accessibility in a resource-constrained academic environment. Developed using the Flutter framework with Firebase backend services, the system incorporates university-based authentication, structured item reporting, administrative validation, and real-time push notifications. The study followed an Agile Software Development Life Cycle (SDLC) framework and evaluated system quality based on ISO/IEC 25010 criteria through functional testing and a Likert-scale survey administered to 48 CEU students, faculty, and staff. Results indicate strong performance across functionality, usability, reliability, security, compatibility, and maintainability dimensions. The findings demonstrate that integrating CNN-assisted image recognition into campus service applications provides a scalable and efficient approach to modernizing lost-and-found operations in higher education institutions.

Keywords:

digital lost-and-found; convolutional neural network; mobile application; campus information systems; image recognition; ISO/IEC 25010