Research Advancements in Recycling: YOLOV4 and Darknet-powered Object Detection of Hazardous Items

Abstract

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.