English(EN) Edge-Optimized YOLOv11n Architecture for Offline Recognition of Nigerian Sign Language Alphanumeric Gestures

新型YOLOv11n架构赋能低资源移动设备上的尼日利亚手语识别

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员开发了一种轻量级的YOLOv11n架构，专为在低资源移动设备上实时识别尼日利亚手语而设计。该框架通过针对内存有限的硬件（如标准Android手机）进行优化，解决了现有模型的计算需求。该系统在包含超过11,000张手势图像的数据集上进行了训练，并展示了高精度和高召回率，通过移动应用程序实现了离线功能和即时文本显示已识别的手语。 AI

影响这项开发通过在普通移动设备上实现手语识别，有可能显著改善技术基础设施有限地区聋哑人群的可及性。

排序理由该项目描述了一种新架构和特定应用的基准测试结果，符合研究类别。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Adeleke Adeniyan, Abimbola H. Afolayan, Olanrewaju V. Johnson · 2026-11-04 05:00

面向离线识别尼日利亚手语字母数字手势的边缘优化YOLOv11n架构

arXiv:2611.00025v1 Announce Type: cross Abstract: Deaf inclusion across sub-Saharan society faces regional hurdles, with Nigeria as the core empirical focus. Existing recognition models demand heavy computational infrastructure. However, standard mobile phones used locally rarely…

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面向离线识别尼日利亚手语字母数字手势的边缘优化YOLOv11n架构

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