ItemConference Paper

Object Detection Framework for Identifying Suspicious Items in School Environments using YOLOv8

Title
Object Detection Framework for Identifying Suspicious Items in School Environments using YOLOv8
Creator
Pal Pandian, P.; Rout, Ivan Sunit; Rao, H. V. Ramana; Chutke, Sravanthi; Darshan, B.R.; Philip, Sam
Description
The issue of unattended bags, metallic items, and concealed weapons at schools has made school safety a growing issue worldwide. This paper presents a software-driven, deep learning framework, implementing automatic identification of suspicious items within a school environment, using the new YOLOv8 neural net architecture. A proprietary 5,000 image dataset of simulated school corridors and classrooms, with 5 annotated threat classes, was developed. The system attained a mAP of 95.6%, precision of 96.8%, and recall of 94.5%, with 38 fps inference speed using a single GPU. YOLOv5 and Faster RCNN comparisons showed a mAP improvement of 12-15%, along with nearly 2x faster frame throughput for the proposed approach YOLOv8, resulting in lower latency and faster responsiveness. The system works in a real-time framework, producing annotated alert logs and frames with mAP scores above 0.5. Experiments conducted with different levels of clutter and illumination show the system has sufficient robustness for school surveillance use cases. 2025 IEEE.
Source
Proceedings of 5th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2025;pp.1643-1649
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Bounding Boxes; CNN Framework; Deep Learning; Identification of Suspicious Items; Object Detection; Real-Time Detection; School Surveillance; Security Systems; YOLOv8
Coverage
Pal Pandian P., Christ University, Mechanical & Automobile Engineering, Bangalore, India; Rout I.S., Christ University, Mechanical & Automobile Engineering, Bangalore, India; Rao H.V.R., Geethanjali College of Engineering & Technology, Cse (AIML), Hyderabad, India; Chutke S., Anurag University, Electronics & Communication Engineering, School of Engineering, Hyderabad, India; Darshan B.R., Aditya College of Engineering, Electronics and Communication, Madanapalli, India; Philip S., Christ University, Mechanical & Automobile Engineering, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158242-5;
Format
online
Language
English
Type
Conference paper

Citation

Pal Pandian, P.; Rout, Ivan Sunit; Rao, H. V. Ramana; Chutke, Sravanthi; Darshan, B.R.; Philip, Sam, “Object Detection Framework for Identifying Suspicious Items in School Environments using YOLOv8,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25994.

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