1. 中原工学院计算机学院
2. 中原工学院河南省工业互联网安全大数据发展创新实验室
纸质出版:2025
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刘伎昭, 王昭发, 姜辉. 基于Conv-Res-LSTM的空中手写数字识别[J]. 传感器与微系统, 2025,(4):83-87.
刘伎昭, 王昭发, 姜辉. 基于Conv-Res-LSTM的空中手写数字识别[J]. 传感器与微系统, 2025,(4):83-87. DOI: 10.13873/J.1000-9787(2025)04-0083-05.
针对现有基于声波感知的手写数字识别方案中,一般直接将多普勒频移对应的时频图作为图片输入卷积神经网络(CNN)进行特征提取,对时序特征提取不足导致识别准确率较低的问题,提出了一种结合CNN和长短时记忆(LSTM)网络的卷积残差(Conv-Res)-LSTM模型。首先,通过Conv-Res网络对时频图的深度局部特征进行提取,然后,将特征矩阵按列展为时间序列,送入LSTM中提取时序依赖关系。在智能手表上实现了一个系统原型,实验结果表明:手写数字的平均识别精度达到了94.8%
与现有的3种基于经典CNN的方法相比平均提高了3.79%
且平均识别响应时间为167 ms。
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