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May 4, 2026EPJ Web of Conferences0 citationsOpen Access

GreenSort: A Vision-Centric Robotic System for Smart Waste Collection and Environmental Protection using SSD MobileNet V2 and the TACO Dataset

AAAndrea AmalanathanSPSaurav PampanaKKshitij

Key Points

  • The aim is to develop a robotic system for detecting and collecting trash, contributing to environmental sustainability.
  • Utilized SSD MobileNet V2 for waste classification based on size, shape, and position.
  • Implemented a visual collection system for real-time trash detection and segregation.
  • Employed the TACO Dataset to train the TensorFlow model for improved accuracy.
  • Achieved an accuracy of 96% in waste classification using the TensorFlow model.
  • Successfully demonstrated the robot's capability to identify and collect various types of waste effectively.

Abstract

As part of sustainability, care for the environment is necessary to ensure health of everyone. Sadly, pollution and particularly stray waste have turned out to be a major problem for humans and wildlife. All the litter, which is not properly disposed, adds to the destruction of the environment. This research discusses a new solution to this problem: a trash picking robot. The proposed robot uses a visual collection system to detect and collect nearby trash, making it a great tool for multi-area cleaning. Robotics has changed our lives in many ways, saving time, money, and helping in many areas. This robot is designed to analyse the size, shape, and position of items to accurately, pick them up and transfer them to different types of waste classification. The implementation begins by identifying pollution problems and exploring existing solutions. Multiclass segregation of different types of wastes using SSD MobileNet V2 on TACO Dataset is performed. The TensorFlow model provides an accuracy of 96%. Ultimately, the goal is to show that technology plays an important role in keeping our world clean and green.

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Cite This Study

Amalanathan et al. (2026) studied this question.

synapsesocial.com/papers/69f836aa3ed186a739980e69https://doi.org/10.1051/epjconf/202636702007
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