Jeloux P. Docto, Angelika Ice Labininay, J. Villaverde
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引用次数: 1
Abstract
An estimate of 1.3 billion people suffers visual problems, which causes a lower quality of life because sight is recognized to be vital for humans and is core in assisting them in their day-to-day activities. The study proposes a system to develop a third eye-hand glove object detection for visually challenged people with the You Only Look Once (YOLO)v4-tiny algorithm that detects indoor objects. The system captures the image using the camera attached to the Raspberry Pi 4B will be fed to the system. The object detection process will then proceed to identify object types. Distance estimation comes afterward to calculate the distance of the identified object away from the camera, both outputted through an audio output. The system included forty (40) tests of the objects from the Common Objects in Context (COCO) dataset found indoors. The system's overall F1 score, precision, recall, and accuracy is 83.00%.
使用You Only Look Once (YOLO)v4-Tiny算法的视障人士第三眼手手套目标检测
估计有13亿人患有视力问题,这导致生活质量下降,因为人们认为视力对人类至关重要,是帮助人们进行日常活动的核心。该研究提出了一种系统,用于开发第三眼-手-手套物体检测系统,该系统使用You Only Look Once (YOLO)v4-tiny算法来检测室内物体。系统使用附着在树莓派4B上的摄像头捕获图像,并将其馈送到系统中。然后,对象检测过程将继续识别对象类型。距离估计随后计算识别对象到相机的距离,两者都通过音频输出输出。该系统包括40个测试对象,这些对象来自室内环境中的公共对象(COCO)数据集。该系统的F1总分、准确率、召回率和准确率为83.00%。