https://journals.itiud.org/index.php/paradigmplus en-US haflorezf@udistrital.edu.co (Hector Florez) christian.grevisse@uni.lu (Christian Grévisse) Tue, 19 Mar 2024 21:58:44 +0000 OJS 3.1.2.4 http://blogs.law.harvard.edu/tech/rss 60 https://journals.itiud.org/index.php/paradigmplus/article/view/56 <p>With the rise of deep learning technologies, the landscape of recycling plant automation has dramatically transformed. This paper presents a pioneering application of YOLOv4 in conjunction with Darknet, tailored specifically to detect hazardous items including spray cans, batteries, and other potential threats. By introducing this state-of-the-art detection mechanism, recycling plants can elevate safety standards, optimize operational processes, ensure adherence to stringent regulatory guidelines, and play a pivotal role in environmental preservation.</p> Allah Rakhio Copyright (c) 2024 ParadigmPlus https://creativecommons.org/licenses/by/4.0 https://journals.itiud.org/index.php/paradigmplus/article/view/56 Tue, 19 Mar 2024 21:58:18 +0000