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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Vision-based dirt distribution mapping using deep learning.

Ishneet Sukhvinder Singh1,2, I D Wijegunawardana1, S M Bhagya P Samarakoon1

  • 1Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore.

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|August 6, 2023
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This study introduces a vision-based system using YOLOv5 and DeepSORT to classify dirt types for robotic cleaning. It creates a dirt distribution map to improve robot efficiency and prevent damage from incompatible cleaning tasks.

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision

Background:

  • Robotic cleaning systems face efficiency issues and potential damage when encountering incompatible dirt types.
  • Classifying cleaning tasks and assigning them to appropriate robots is a key research area.

Purpose of the Study:

  • To develop a vision-based system for detecting and classifying dirt to optimize robotic cleaning operations.
  • To create a dirt distribution map for efficient task allocation among cleaning robots.

Main Methods:

  • Utilized YOLOv5 for object detection and DeepSORT for tracking to identify and classify different types of dirt.
  • Developed a dirt distribution map indicating regions requiring specific cleaning actions.

Main Results:

  • The proposed vision-based system achieved a high accuracy of 81.0% in dirt indication for the distribution map.
  • The dirt distribution map facilitates collaborative cleaning frameworks for uninterrupted and energy-efficient robot deployment.

Conclusions:

  • The developed system effectively classifies dirt, enabling intelligent task assignment for robotic cleaners.
  • This approach enhances the efficiency and operational lifespan of cleaning robots across various surfaces and dirt types.