ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Software
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1535775
This article is part of the Research TopicHarnessing Visual Computing to Revolutionize Manufacturing Efficiency and InnovationView all articles
Computer Vision and AI-based Cell Phone Usage Detection in Restricted Zones of Manufacturing Industries
Provisionally accepted
Uttam U Deshpande*
Supriya Shanbhag
Ramesh KotiAmeet ChateSudhindra DeshpandePavan KulkarniNeha GanigerVarad Rasane- KLS Gogte Institute of Technology, Belagavi, Belagavi, Karnataka, India
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Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones' growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauthorized mobile phone use in these environments can lead to significant safety hazards, operational disruptions, and security breaches. There is an urgent need to develop an intelligent system that can identify the presence of individuals as well as cellphone usage. We propose an advanced Artificial Intelligence and Computer Vision-based real-time cell phone detection system to detect mobile phone usage in restricted zones. Modern deep learning approaches, such as YOLOv8 for real-time object detection to accurately detect cell phone usage, are combined with dense layers of ResNet-50 to perform image classification tasks. We highlight the critical need for such detection systems in manufacturing settings and discuss the specific challenges encountered. To support this research, we have developed a custom dataset of 2150 images, which features a diverse array of images with varying foreground and background elements to reflect real-world conditions. Our experimental results demonstrate that YOLOv8 achieves a Mean Average Precision (mAP50) of 49.5% at 0.5 IoU for cellphone detection tasks and an accuracy of 96.03% for prediction tasks. These findings underscore the effectiveness of our AI and CV-based system in detecting unauthorized mobile phone usage in restricted zones.
Keywords: artificial intelligence, Computer Vision, YOLOv8, Resnet-50, Cell phone detection
Received: 27 Nov 2024; Accepted: 07 Apr 2025.
Copyright: © 2025 Deshpande, Shanbhag, Koti, Chate, Deshpande, Kulkarni, Ganiger and Rasane. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Uttam U Deshpande, KLS Gogte Institute of Technology, Belagavi, Belagavi, 590008, Karnataka, India
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
