Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Pal Pandian, P.; Rout, Ivan Sunit; Rao, H. V. Ramana; Chutke, Sravanthi; Darshan, B.R.; Philip, Sam Title A name given to the resource Object Detection Framework for Identifying Suspicious Items in School Environments using YOLOv8 Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 5th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2025;pp.1643-1649 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECMSN68058.2025.11383365" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECMSN68058.2025.11383365</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035489707?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035489707?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pal Pandian P., Christ University, Mechanical &amp; Automobile Engineering, Bangalore, India; Rout I.S., Christ University, Mechanical &amp; Automobile Engineering, Bangalore, India; Rao H.V.R., Geethanjali College of Engineering &amp; Technology, Cse (AIML), Hyderabad, India; Chutke S., Anurag University, Electronics &amp; Communication Engineering, School of Engineering, Hyderabad, India; Darshan B.R., Aditya College of Engineering, Electronics and Communication, Madanapalli, India; Philip S., Christ University, Mechanical &amp; Automobile Engineering, Bangalore, India Description An account of the resource 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. Subject The topic of the resource Bounding Boxes; CNN Framework; Deep Learning; Identification of Suspicious Items; Object Detection; Real-Time Detection; School Surveillance; Security Systems; YOLOv8 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158242-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online