基于注意的统一架构,用于在交通面板上检测阿拉伯文本,以推进自然场景中的自主导航
Basma M Hassan1,2, Samah A Gamel3, Fatma M Talaat4,5
1Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt. basma.mohamed@ai.kfs.edu.eg.
Scientific reports
|October 1, 2025
概括
本研究介绍了一种基于注意力的模型,用于检测和识别交通标志上的阿拉伯文本,这对于自主导航至关重要. 该系统实现了高精度,提高了高级驾驶辅助系统 (ADAS) 的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 自主导航系统需要在各种环境中准确识别文本.
- 阿拉伯文字带来了独特的挑战,因为它具有曲线性质和复杂的字符变化.
研究的目的:
- 开发一个统一的,基于注意力的架构,用于在交通板上检测和识别阿拉伯文本.
- 解决阿拉伯文本在现实世界自动驾驶场景中的特定挑战.
主要方法:
- 利用一种新的架构,将卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 与注意力机制相结合.
- 在ASAYAR数据集上训练和评估模型,包括各种阿拉伯文本样本和注释.
主要成果:
- 实现了最先进的性能,整个欧盟的平均交叉点 (IoU) 为0.9505.5.
- 证明了高精度 (0.953),回忆 (0.934),F1得分 (0.929) 和97%的识别准确度.
- SHAP分析证实了模型的可解释性,并专注于相关特征.
结论:
- 拟议的模型为阿拉伯文本检测和识别提供了一个强大的和可解释的解决方案.
- 它的计算效率和实时适用性适用于高级驾驶辅助系统 (ADAS) 和自动驾驶汽车.
- 这项研究为复杂的现实应用程序提供了可扩展的多语言文本检测系统.
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