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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Using Object Detection Technology to Identify Defects in Clothing for Blind People.

Daniel Rocha1,2,3, Leandro Pinto2, José Machado4

  • 1Algoritmi Research Centre/LASI, University of Minho, 4800-058 Guimarães, Portugal.

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|May 13, 2023
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Summary
This summary is machine-generated.

This study introduces a computer vision system using You Only Look Once (YOLO) object detection to identify clothing defects like stains for visually impaired individuals. The technology effectively detects garment flaws, aiding the blind community in clothing management.

Keywords:
YOLOv5blind peopleclothing defect detectiondeep learningobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visually impaired individuals face significant challenges in identifying clothing defects such as stains or holes.
  • Advancements in computer vision offer opportunities to mitigate these limitations and enhance clothing management for the blind community.

Purpose of the Study:

  • To develop and evaluate an object detection system for categorizing and detecting stains on garments.
  • To leverage the You Only Look Once (YOLO) architecture for automated defect inspection in clothing.

Main Methods:

  • Collected a dataset of clothing defects, including various stains and conditions.
  • Optimized the defect detection system through dataset expansion, data augmentation, and defect classification.
  • Compared and evaluated three distinct YOLOv5 models for performance.

Main Results:

  • The proposed YOLO-based system demonstrated high average precision (AP) in detecting garment defects.
  • The approach proved effective across diverse and challenging defect detection scenarios.
  • The system's performance indicates suitability for real-world applications.

Conclusions:

  • The developed defect detection system is effective for assisting visually impaired individuals with clothing management.
  • The research paves the way for a mobile application to enhance accessibility for the blind community.
  • Object detection technology shows significant potential in creating assistive tools for daily living tasks.