Advances in ASL Detection: a YOLOv8-based framework

被引：0

作者：

Emam, Mohamed Hesham ^{[1
]}

Ghaly, Abdelrhamn ^{[1
]}

Mostafa, Ahmed ^{[1
]}

Ayman, Ali ^{[1
]}

Adel, Aya ^{[2
]}

Tawfik, Mohamed ^{[3
]}

Badawy, Wael ^{[3
]}

机构：

[1] Egyptian Russian Univ, Sch Artificial Intelligence, Cairo, Egypt

[2] Egyptian Russian Univ, Sch Artificial Intelligence, Dept Artificial Intelligence, Cairo, Egypt

[3] Egyptian Russian Univ, Dept Data Sci, Sch Artificial Intelligence, Cairo, Egypt

来源：

2024 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND SMART INNOVATION, ICMISI 2024 | 2024年

关键词：

American Sign Language "ASL" recognition; YOLOv8; algorithm; real-time gesture detection; assistive technology; sign-to-text translation; AMERICAN SIGN-LANGUAGE; RECOGNITION;

D O I：

10.1109/ICMISI61517.2024.10580068

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present in this paper a YOLOv8-based framework to accurate detection of American Sign Language "ASL". ASL is widely used in bridging the "communication" among hearing communities. The proposed framework provides a real-time detection of gesture in a complex environment. The proposed framework has been development, implemented, and tested using 6033 images with size of 416x416. It contains 36 classes, 26 classes for Letters ('A' - 'Z'), and 10 classes for Numbers ('0' - '9'). The training dataset have been augmented to better increase the accuracy of detection. The simulation results shows that the efficiency of detecting in a high frame rate.

引用

页码：140 / 143

页数：4

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