通过VAS-Compass Net提高情境意识,以识别定向车辆警报声音
Chiun-Li Chin1, Jun-Ren Chen2, Wan-Xuan Lin2
1Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
一个新的声音警报辅助系统 (SAAS) 帮助有听力障碍的人检测车辆声音及其方向. 该系统通过提高弱势群体的局势意识来提高道路安全.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 听力学 听力学是指听力学.
背景情况:
- 听力障碍者在交通中面临重大风险,原因是听觉感知受损.
- 现有的辅助设备,如助听器和耳植入器,在成本和功能上都有局限性.
研究的目的:
- 开发一个创新的声音警报辅助系统 (SAAS) 以提高局势意识和道路安全.
- 为有听力障碍的人创造一个具有成本效益和可访问的解决方案.
主要方法:
- 提出了VAS-Compass Net,集成了EfficientNet-lite0,MobileNetV3-Small和GhostNet卷积神经网络. 这是一个很好的例子.
- 使用模糊排名组合技术进行声音分类和方向识别.
- 将音频信号转换为光谱图和Mel频 cepstral 系数图像,通过图像拼接进行融合.
- 在Raspberry Pi 5上部署模型用于边缘计算,与智能手表配对.
主要成果:
- 在基于服务器的计算中,VAS-Compass Net实现了84.38%的准确性,在边缘计算中达到83.01%.
- 成功识别了各种车辆警报声音及其来源的方向.
- 在微型计算机上实践应用实时部署的可行性.
结论:
- 开发的SAAS有效地提高了听力障碍患者的情境意识和警觉.
- 该系统提供了一种有前途的方法,可以显著提高这一人口群体的道路安全.
- 边缘计算部署使得日常使用的实用和可访问的解决方案成为可能.
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