Using Object Detection Technology to Identify Defects in Clothing for Blind People

Abstract

Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community.

A Smart Cane Based on 2D LiDAR and RGB-D Camera Sensor-Realizing Navigation and Obstacle Recognition

“…YOLOv5 [48] was proposed by Ultralytics LLC (Washington, DC, USA), which has been widely used in the assistance system for the visually impaired with target recognition functions. It is used to identify objects such as pedestrian crossings [49,50], traffic lights [51], buses [52], straight or winding paths [21], clothing defects [53], stairs and roads [54], faces and money [55], and indoor fires [56]. Since the official model of YOLOv5 alone cannot meet the requirements of this work to identify all obstacles on the road and improve the training speed of the YOLOv5 model, in this paper, we increased the training set of the guide system model and added the attention machine [57] system to the YOLOv5 algorithm to make improvements.…”

Automatic system to identify and manage garments for blind people

“…The process of identifying features on garments is often slow and challenging, leading to a loss of autonomy in choosing their desired clothing. This study aims to address these challenges by proposing an automatic wardrobe that improves quality of life and well-being of blind people, complementing the continuous work developed under the same project [4]- [11], [14]. In this research, the major contribution is the development of a mechatronic system prototype that facilitates garment selection and management.…”