An IoT‐based human detection system for complex industrial environment with deep learning architectures and transfer learning

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE An IoT-based human detection system for complex industrial environment with deep learning architectures and transfer learning Imran Ahmed, orcid.org/0000-0002-7751-286X Center of Excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan Imran Ahmed, Marco Anisetti, and Gwanggil Jeon contributed equally.Search for more papers by this authorMarco Anisetti, orcid.org/0000-0002-5438-9467 Dipartimento di Informatica (DI), Università degli Studi di Milano, Milano, ItalySearch for more papers by this authorGwanggil Jeon, Corresponding Author ggjeon@gmail.com orcid.org/0000-0002-0651-4278 School of Electronic Engineering, Xidian University, Xi'an, China Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea Correspondence Gwanggil Jeon, Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea. Email: ggjeon@gmail.comSearch for more papers by this author Imran Ahmed, orcid.org/0000-0002-7751-286X Center of Excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan Imran Ahmed, Marco Anisetti, and Gwanggil Jeon contributed equally.Search for more papers by this authorMarco Anisetti, orcid.org/0000-0002-5438-9467 Dipartimento di Informatica (DI), Università degli Studi di Milano, Milano, ItalySearch for more papers by this authorGwanggil Jeon, Corresponding Author ggjeon@gmail.com orcid.org/0000-0002-0651-4278 School of Electronic Engineering, Xidian University, Xi'an, China Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea Correspondence Gwanggil Jeon, Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea. Email: ggjeon@gmail.comSearch for more papers by this author First published: 24 May 2021 https://doi.org/10.1002/int.22472 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Artificial intelligence (AI), combined with the Internet of Things (IoT), plays a beneficial role in various fields, including intelligent surveillance applications. With IoT and 5G advancement, intelligent sensors, and devices in the surveillance environment collect large amounts of data in the form of videos and images. These collected data require intelligent information processing solutions, help analyze the recorded videos and images to detect and identify various objects in the scene, particularly humans. In this study, an automated human detection system is presented for a complex industrial environment, in which people are monitored/detected from a top view perspective. A top view is usually preferred because it can provide sufficient coverage and enough visibility of a scene. This study demonstrates the applications, efficiency, and effectiveness of deep learning architectures, that is, Faster Region Convolutional Neural Network (Faster R-CNN), Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv3), with transfer learning. Experimental results reveal that with additional training and transfer learning, the performance of all detection architectures is significantly improved. The detection results are also compared using the same data set. The deep learning architectures achieve promising results with maximum true-positive rate of 93%, 94%, and 94% for Faster-RCNN, SSD, and YOLOv3, respectively. Furthermore, a detailed study is performed on output results that highlight challenges and probable future trends. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation