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In urban areas, where waste production often surpasses outdated human sorting methods, efficient trash management is becoming more and more challenging. Using a Raspberry Pi and a refined YOLOv8 object detection model, we provide a real-time garbage identification solution. This hybrid technique enhances detection performance for localized waste categories, including metal, batteries, and plastic containers, by combining pre-trained YOLOv8 weights with unique fine-tuning on a domain-specific dataset. A Flask-based web application provides users with a straightforward monitoring interface. Additionally, when waste is identified, the technology automatically notifies collection staff via WhatsApp, guaranteeing prompt notifications.

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An IoT Based Real-Time Garbage Detection System Using YOLOv8 and Raspberry Pi

  • 会议论文

  • 发表日期：

    2026年

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  • Samarth Yogesh Jadhav,
  • Rutuja Rajaram More,
  • Rohit Dnyaneshwar Kokate,
  • Aditi Dinkar Kadam,
  • Nikhil Subhash Patankar

摘要

In urban areas, where waste production often surpasses outdated human sorting methods, efficient trash management is becoming more and more challenging. Using a Raspberry Pi and a refined YOLOv8 object detection model, we provide a real-time garbage identification solution. This hybrid technique enhances detection performance for localized waste categories, including metal, batteries, and plastic containers, by combining pre-trained YOLOv8 weights with unique fine-tuning on a domain-specific dataset. A Flask-based web application provides users with a straightforward monitoring interface. Additionally, when waste is identified, the technology automatically notifies collection staff via WhatsApp, guaranteeing prompt notifications.