用深度学习与可解释AI提升阿拉伯手语识别准确率与透明度
Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition
- 融合MobileNetV3等模型与XAI技术，增强手语识别可解释性
- EfficientNet-B2在两个数据集上分别达99.48%与98.99%准确率
- 适合医疗、教育等领域，推动无障碍沟通技术落地
本研究提出一种集成方法，利用MobileNetV3、ResNet50和EfficientNet-B2等先进深度学习模型识别阿拉伯手语（ArSL）。通过引入可解释AI（XAI）技术提升模型决策透明度。实验使用ArSL2018和RGB阿拉伯字母手语（AASL）数据集，EfficientNet-B2分别取得99.48%和98.99%的最高准确率。关键创新包括针对类别不平衡的复杂数据增强策略、分层5折交叉验证以提升泛化能力，以及使用Grad-CAM实现模型决策过程可视化。该系统不仅刷新识别精度基准，更强调可解释性，适用于医疗、教育及包容性通信技术场景。
原文摘要 · Abstract (English)
This study introduces an integrated approach to recognizing Arabic Sign Language (ArSL) using state-of-the-art deep learning models such as MobileNetV3, ResNet50, and EfficientNet-B2. These models are further enhanced by explainable AI (XAI) techniques to boost interpretability. The ArSL2018 and RGB Arabic Alphabets Sign Language (AASL) datasets are employed, with EfficientNet-B2 achieving peak accuracies of 99.48\% and 98.99\%, respectively. Key innovations include sophisticated data augmentation methods to mitigate class imbalance, implementation of stratified 5-fold cross-validation for better generalization, and the use of Grad-CAM for clear model decision transparency. The proposed system not only sets new benchmarks in recognition accuracy but also emphasizes interpretability, making it suitable for applications in healthcare, education, and inclusive communication technologies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。