YOLOv8-Based Model for Solid Waste Detection

• Autores: Guevara Saldaña, Rodrigo Alonso, Díaz Tomás, Marcos Iván, Torres Villanueva, Marcelino
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/160
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/160; https://doi.org/10.48168/innosoft.s16.a160; https://purl.org/42411/s16/a160; https://n2t.net/ark:/42411/s16/a160
• Nivel de acceso: acceso abierto
• Materia: Detection; Deep Learning; Solid Waste; YOLO; Detección; Aprendizaje Profundo; Residuos Sólidos

The primary focus of this article was to employ Ultralytics technology, specifically YOLOv8, in object recognition. This involved utilizing supervised learning and other machine learning techniques. The article took into consideration the definitions of object detection and model training to effectively categorize solid waste, thereby facilitating recycling efforts. Following this, each object class was manually identified using the LabelImg tagger, considering the positions of the objects within the images. This approach led to the analysis of 1517 images and produced notably high-quality and significant results.