基于多层脉冲神经网络的非接触液位检测方法  

Non-contact Liquid Level Detection Method Based on Multilayer Spiking Neural Network

作　　者：张季伦 朱毅 李颖 陈方 刘颖 屈鸿[1] ZHANG Jilun;ZHU Yi;LI Ying;CHEN Fang;LIU Ying;QU Hong(School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Beijing Xiaomi Mobile Software Co.,Ltd,Beijing 100085,China)

机构地区：[1]电子科技大学计算机科学与工程学院,成都611731 [2]北京小米移动软件有限公司,北京100085

出　　处：《电子与信息学报》2023年第8期2759-2769,共11页Journal of Electronics & Information Technology

基　　金：国家重点研发计划(2018AAA0100202);四川省科技计划(2022YFG0313)。

摘　　要：尽管基于深度学习的非接触液位检测方法能够较好地完成检测任务,但其对计算资源的较高要求使其不适用于算力受限的嵌入式设备。为解决上述问题,该文首先提出了基于多层脉冲神经网络的非接触液位检测方法;其次,提出了单帧和帧差脉冲编码方法将视频流时间动态性编码成可重构的脉冲模式;最后在实际场景中对模型进行测试。实验结果表明,所提方法具有较高应用价值。Although the non-contact liquid level detection method based on deep learning can perform well,its high demand on computational resources makes it not suitable for embedded devices with limited resource.To solve this problem,a non-contact liquid level detection method is first proposed based on multilayer spiking neural network;Furthermore,spiking encoding methods based on single frame and frame difference are proposed to encode the temporal dynamics of video stream into reconfigurable spike patterns;Finally,the model is tested in the real scene.The experimental results show that the proposed method has high application value.

关 键 词：脉冲神经网络 模式识别 LIF神经元 脉冲编码 非接触液位测量 

分 类 号：TN911.73[电子电信—通信与信息系统]